Research ArticleA Study on the Characteristics of MicroseismicSignal during Deformation and Failure of DifferentMaterials under Uniaxial Compression

Chengwu Li1 Xiaomeng Xu12 Lihui Dong1

Mohammad Sarmadivaleh2 and Xiaoyuan Sun13

1Faculty of Resources and Safety Engineering China University of Mining and Technology Beijing 100083 China2Department of Petroleum Engineering Curtin University of Technology Perth WA 6151 Australia3School of Safety and Environment Taiyuan University of Science and Technology Taiyuan 030024 China

Correspondence should be addressed to Xiaomeng Xu xxmsafety163com

Received 5 June 2015 Revised 5 October 2015 Accepted 8 October 2015

Academic Editor Jose A Becerra Villanueva

Copyright copy 2016 Chengwu Li et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

With the decline of shallow coal reserves and increase in demand of coal consumption the hazard of coal and rock dynamicdisasters is expanding In this paper we conducted some laboratory scale tests on coal cement and glass materials to figure outthe microseismic (MS) characteristic differences among materials A new method denoted as WPT-LMD is proposed to conductsignal denoising and analysis work A series of basic analyses regarding MS are conducted including the relationships betweenMS characteristics and loading rate coal powder particle size loading stage and MS event statistics Research results show thatthe damage of all these three materials is accompanied with MS events abundant with low frequency component (0ndash200Hz) Butcement and coal specimens produce with relative wide frequency distribution in sample frequency domain while glass specimenswere found to only produce low frequency eventThe powder particle sizes have obvious influence on the strength of coal specimenbut the loading rate seems to have no influence on theMS characteristicsThe outcome of the paper will further provide a theoreticalbasis for understanding the mechanism of MS activities and has great significance to improve the coal mining safety

1 Introduction

It is generally known that many solidmaterials emit low-levelseismic signals during their deformation anddamage processand this phenomenon is generally called acoustic emission(AE) at the laboratory specimen scale or microseismicity(MS) at the rock massive scale (denoted as MS in this workfor unity) [1 2] In the coal mining industry microseismicmonitoring techniques are usually utilized in detecting thefracture and failure of large area of rocks and finally realizingthe forecast and prevention of dynamic disasters (rock burstand coal and gas outburst) [3] As a result most deep coalmines in China have installed MS monitoring system toobserve the abnormal microseismic activities during theblasting tunneling drilling top coal caving and excavatingprocess [4 5] It is proved that MS technique is a novel tool tobe used as a remote proxy for daily safety monitoring at rock

burst-prone mines But in recent years there are still nearly200 causalities caused by dynamic disasters every year in thecoal mining industry of China [6] That is to say there is stillan urgency to figure out how to use MS more efficiently andrealize accurate real-time prealarming for dynamic disasters

Many research studies have been conducted on boththe mechanism and characteristics analysis of microseismicactivities Cook put forward utilizing MS technique to solvethe rock burst problems and in his following works he tried tofigure out the mechanism of MS [7ndash9] Yamada et al studiedthe relationship between AEMS event counts and sampledamage during the loading process of coal and rock [10]Lu and some other researchers conducted a series laboratorymodelingwork to simulate the field site rock burst forecastingwithMS [3 4]The relationship between frequency-spectrumcharacteristics of MS events and the damage evolution law ofmaterial during the uniaxial and triaxial compression loading

Hindawi Publishing CorporationShock and VibrationVolume 2016 Article ID 6751496 12 pageshttpdxdoiorg10115520166751496

2 Shock and Vibration

were also studied in the laboratory test scale [11 12] Howeverthe comparison of characteristics of MS events betweendifferent materials is merely considered and represented instudies above But this is very important for improvingthe accuracy of prealarming work underground This ismainly because the coal seam has great differences with itssurrounding rocks and due to the diversity of mechanicproperties the roof floor rocks and coal seam may generatequite different patterns of seismicity [3]

To study the MS characteristics of different materials wechoose the cement paste coal and glass to make specimensand perform some uniaxial compression loading on thesesamples and this kind of test was rarely found in the previousstudies There are several considerations for our decisions ofusing these threematerials Actually we selected themmainlyfor their distinguished mechanic properties Glass is selectedfor it is a type of typical brittle material it will be fractureddirectly under critical stress so here it is used to presentthe extremely brittle rocks [13] Coal is also kind of brittlerock but previous studies showed that coal would experiencea process of compaction before showing its linear elasticfeature and only after reaching the strain-soft stages did theprimary damage begin [3] The artificial briquette specimenwill be slightly different to natural coal bulks for it is morehomogeneous and isotropic but the main loading patternsare the same as natural bulks Cement paste specimens arewidely used in the research studies of construction and civilengineering issues the selection of cement paste means wehave more standard references and results to be comparedAlso in the underground field site there are many artificialconstructions with concrete and cement these nonnaturalstructures will also produce someMS signal whichmay affectthe accuracy of MS monitoring In summary we believe thatthese three kinds of material should be well represented fordifferent material properties

This paper mainly conducts experiment studies on mate-rial deformation damage and AEMS feature under uniaxialloading circumstance A new method by combining thewavelet packet transform (WPT) and local mean decompo-sition (LMD) is proposed to denoise and analyze MS signalOn the basis of the application of the WPT-LMD methodthe characteristics of MS signal produced by the deformationand damage of coal cement and glass samples are comparedand analyzed The remainder of this paper is summarizedas follows Section 2 describes the theory and applicationprocedure of WPT-LMD method In Section 3 the detailsof sample preparation and the procedure of laboratory testare presented In Section 4 the experiment results of severaltypical tests of different materials are illustrated Discussionsfor the effects of material type loading rate and coal particlesize onMS characteristics andMS event parameters statisticsare given in Section 5 Conclusion and future work arepresented in Section 6

2 MS Signal Processing Method

21 Combination of WPT and LMD A basic analysis onthe laboratory test data shows that the original signal is

partially swamped by the ambient noises Moreover thedominant frequency distribution range of MS signals herewas relatively wide both low frequency (lt100Hz) and highfrequency (500ndash1500Hz) compositions exist in the wholeeffective sample frequency domain Consequently it becomeshard to separate the real event signals from backgroundnoises by using sets of filters normally as used in the previousstudies such as some moving average filter Wiener filteror low-pass filter based on Fourier transform [14ndash17] Sowe developed a new method in this work which is actuallythe combination of wavelet packet transform (WPT) andlocal mean decomposition (LMD) This method is denotedas WPT-LMD method here and it is selected to performsignal denoising and analyzing throughout this work Theprinciple and algorithm of WPT have been widely discussedand presented in a lot of literatures [18 19] To be simpleonly the information of LMD is briefly described here but thedetails of its theory and algorithm could be found somewhereelse [17 20]

Local mean decomposition firstly proposed in 2005 isa new adaptive time-frequency analysis method [17] It isfamous for its good performances in dealing with nonlinearand nonstationary signals The basic algorithm of LMD isvery similar to empiricalmode decomposition (EMD) whichis developed earlier and widely applied in various studiesBut several investigations conclude that LMD is better thanEMD in signal processing especially for solving the endpointeffect and avoiding the mode mixing problems [20 21]LMD decomposes signal into a set of product functions(PFs) which represents characteristics of frequency band andenergy distribution [17 20] Dealing with the PFs it is easyto deny or filter the signal calculate instantaneous frequency(IF) and reconstruct reserved PFs into a new signal Basedon both the algorithms of WPT and LMD we successfullyactualized the data processing with WPT-LMDmethod andall coding works were conducted with MATLAB software

22 MS Signal Processing Procedure To start with WPTmethod is firstly adopted to conduct a soft threshold globaldenoising and then the denoised data are decomposed to findthe best tree structure (level 5) [19] The best tree nodes of acoal specimen (CSA2) are shown in Figure 1 To find the bestmother wavelet a denoising effect comparison among db5db8 and sym6 wavelet function is made and finally db8 ischosen

In the following process LMD method is performed onthe decomposition signal of each tree node and a seriesof PFs for every node are obtained Some PFs that mainlycontain environment noises will be determined as ineffectivedecomposition and removed while others will be judgedas effective PFs and reserved To determine whether a PFis an effective signal or noise decomposition the conceptof contribution coefficient is introduced The definition ofcontribution coefficient is shown in

119888119894=

1003816100381610038161003816119903119894

1003816100381610038161003816sdot 119890119894

sum119899

119894=1

1003816100381610038161003816119903119894

1003816100381610038161003816sdot 119890119894

(1)

Shock and Vibration 3

0

01

02

After WPT-LMD denoising

Original data (before denoising)

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plitu

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V)

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plitu

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V)

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S

A approximate signalD detail signal

A1

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D21 D22

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D41

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Figure 1 TheWPT best tree of CSA2 and denoising effect of WPT-LMDmethod

where 119888119894is referred to as the 119894th contribution coefficient 119894

is the number of PFs 119899 is the total number of PFs 119903119894is the

correlation coefficient between 119894th PF and original signal and119890119894is the occupation ratio of energy of 119894th PF only when 119888

119894

is larger than the critical value 0001 the PF is selected aseffective decomposition

All effective PFs preserved are combined to reconstructthe new tree nodes and then all nodes are reconstructed andgot the denoised signal series A comparison between theoriginal signal and denoised signal is also shown in Figure 1

Finally LMD method is used again on the denoisedsignal Some basic analysis could be easily done with the finalPFs such as the characteristics of frequency spectrum of thewhole signal or the features of the energy distribution andinstantaneous frequency of each PF

3 Experimental Setup

31 Experimental Equipment and Characteristic ParameterThe experimental testing system was comprised of stressloading equipment and data acquisition apparatus As shownin Figure 2 the EHF-UG500KN type servo press machine(Shimadzu Corp Japan) was used to provide the uni-axial compressive loading environment While the coaland rock dynamic disaster monitoring system (ZDKT-1CUMTB2009) was served as seismic activities monitoringfacilities and data acquisition module in this work ZDKT-1

is a real-time monitoring system developed for the forecastof coal and rock dynamic disasters in a coal mining field siteThis system is multichannel and multiparameter monitoringsystem and it can also be used in the laboratory test Elec-tromagnetic geomagnetic and microseismic transducersstress and strain gauges and gas concentration sensors areall included in the whole system It totally has 12 channelswith a 51 kHz maximum data sample rate In this work thesample rate was set as 3000Hz to digitize filter and storedata automatically

To describe characteristics of MS signal several basicparameters for both process parameters and state parametersare included in this work These parameters are ampli-tude frequency MS event counts and event rate energyaccumulation and energy release rate Actually all theseparameters are commonly used in the traditional MSAEstudies and their basic definition could be found somewhereelse [2] To get the values of these parameters the STALTA(shortlong term average) method was used to recognize theMS signal [22] Then every individual MS event detectedwas extracted from the total data and this will be used forfurther quantitative analysis on characteristic parametersWehave set the STA and LTA energy window lengths at 10msand 200ms respectively As the sample rate is 3000Hz theshort series number should be 30 and long series number is600 and the trigger threshold value for STA and LTA ratio isselected as 5

4 Shock and Vibration

(a) (b) (c)

Figure 2 Testing system (a) loading equipment (b) ZDKT-1 dynamic disaster monitoring system (c) data acquisition software

Figure 3 Original and damaged patterns of different types of specimens

32 Specimen Preparation Three types of specimens madeby different materials were tested in uniaxial compressionbriquette coal specimens cement specimens (pure Portlandcement) and glass specimens The original and damagedpatterns of all these three types of specimens are shown inFigure 3

Coal blocks were gathered from Pingdingshan 10 coalcolliery where strong dynamic disasters often occurredThese coal bulks are crushed and sieved into 05mmndash2mm025ndash05mm and less than 025mm particle powder indiameter After that some water and coal tar were mixedinto coal powder and the proportion by weight is 05 1 20(water coal tar coal powder) Then the mixed powder wascast in the steel molds and stressed under 10MPa axialpressure A small amount of wax was attached to the internalsurface of the mold before making every specimen to getout the shaped briquette sample easily To keep the goodmechanical consistency between these artificial samplesseveral measures were adopted (1) all specimens were madewith the same size mold (100 times 100 times 100) and the qualitiesof coal powder and coal tar were strictly controlled (2) Allspecimens were kept stressed under 10MPa axial loadingpressure for up to 8 hours (3)The specimens were removed

from the molds 24 hours after unloading the stress and curedfor 28 days in a curing room Totally there were 9 briquettespecimens made 3 for each particle size

In contrast two kinds of cement Portland 425 and425R types were selected to make cement specimens Thewhole process of making cement specimen is similar tothat of coal but the mix proportion for cement paste byweight is 035 1 (water cement) Also there was no externalloading conducted when making this kind of specimensThese specimens were removed from the mold after castingand cured for 28 days at 20 plusmn 2∘C Glass specimens weremade by gluing 20 layers of glass together with glass cementwith 5mm thickness for each piece Still for cement and glassspecimens there were 3 prepared for each type and they wereall in the same size of 100 times 100 times 100mm in cubic shape

33 Test Procedure The whole experiment process consistedof the following steps

(1) Place the specimens on the centre of the platform oftesting machine and attach microseismic transducerto the surface of specimen

Shock and Vibration 5

Table 1 Experimental parameters of specimens and work condition details

Specimen number Loading rate (Ns) Peak value (kN) Loading time (s) Special noteCSA2 10 774 1050 Particle size lt025mmCSB1 5 224 270 Particle size 025ndash050mmCSB2 10 272 150 Particle size 025ndash050mmCSB3 20 107 1830 Particle size 025ndash050mmCSC2 10 318 300 Particle size 050ndash200mmCMSA1 150 12322 910 Cement type PO 425CMSA2 200 16296 860 Cement type PO 425CMSA3 250 19659 820 Cement type PO 425GSB1 200 2963 1610 Layer count 10GSB2 250 20448 870 Layer count 10GSB3 300 4793 1630 Layer count 10

(2) Turn on the ZDKT-1 type dynamic disaster monitorapparatus start up the monitoring operation systemand set related initial parameters well for examplethe loading rate sample frequency and initial loadingforce

(3) Before recording the data verify that signal acquisi-tion module worked well and begin to record data

(4) Start the loading process and keep loading until thespecimens were completely damaged

(5) Stop loading and recording save the data file(6) Repeat the procedure again and run test on other

specimens

4 Experimental Result and Analysis

A series of uniaxial compression loading were conducted oncoal cement and glass specimensThe detailed experimentalparameters such as the loading rate loading last time andthe peak value of loading force are shown in Table 1 Dueto the space limitation only partial results of the tests foreach kind of specimen are presented in this paper To bemore specific they are actual 5 tests of coal specimens 3 testsof cement and 3 tests of glass But that is still enough toshow the characteristics changes of MS signal under variousloading conditions For instance CSB1 CSB2 and CSB3 areused for discussion on MS feature changes with the variationof the loading rate CSA2 CSB2 and CSC2 are selectedto give a comparison between samples made by differentparticle size of coal powders and of course MS data recordedfrom different materials are mainly used to compare thecharacteristics of MS events from each other

The material failure process is accompanied by MSAEwhich is actually a form of quick release of stored energySpecimens here were investigated for their mechanical per-formances with microseismicity patterns throughout thewhole loading process Theoretically speaking the continu-ous loading on material will cause the accumulation of strainenergy inside once the accumulated energy exceeds the ulti-mate value limited by the mechanical properties of materialsinternal fracture generation and external damage will occur

Generally there are five detailed stages of the damage anddeformation of materials under uniaxial compression thatis crack closure crack initiation secondary cracking crackcoalescence and crack damage [23] However the actualstress loading patterns will be affected a lot due to themechanical properties of materials The MS activities andloading stress variations with the loading time increase areillustrated in Figure 4 To make it easier to be analyzed eachtest was divided into 10 loading sections according to totalloading timeWe defined sections IndashIII as early stage sectionsIVndashVII as middle stage and sections VIIIndashX as poststage

In case of coal specimens MS activities occurred at bothearly stage and poststage but the primary fracture period ofcoal occurred in the poststage As illustrated in Figure 4 atearly loading stage of coal samples there were several MSactivities produced after that before the peak of loadingstress only few MS activities could be observed most ofthe MS events were collected after the loading peak valueThese briquette coal specimens mostly were destroyed understress less than 10KN and produce MS signals with lowestamplitude for 15mV in comparison with cement and glassspecimensThe patterns of MS events for cement samples arevery similar to coal most activities are collected in primaryfracture and early stage but it should be noticed in themiddleof loading just before the main fracture happens that theMS release is also very active With respect to event rate thevalues for cement samples are lowest in all three materialsFor the glass samples due to their larger uniaxial strengthand layer distribution more MS activities are found throughthewhole loading process Loading on glass samples obtainedthe largest loading stress and the highest MS amplituderanging from 250 kN to 300 kN and approximately 100mVrespectively the same value to cement specimen is rangingfrom 150 kN to 200 kN and 60mV

The LMD result of CSC2 is shown in Figure 5 Thedenoised signal which is obtained by filtering the originalsignal is decomposed into several PFs These PFs havedifferent characteristics in both frequency and time domainThe frequency distributions are separated into three seriesThe high frequency is all collected at the early stage andmainfracture stage with highest amplitude and the frequency isabout 1000ndash1200Hz The medium frequency is the second

6 Shock and Vibration

III

III

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V

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VIIVIII

IXX

CSA2MSF

CMSA1MSF

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MS

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)

Figure 4 The MS events signal with increase of loading stress and time

most abundant withMS events its frequency ranges from 100to 500Hz Low frequency decomposition owns low ampli-tude but it should not be overlooked for low frequency signal(lower than 100Hz) usually spread further in real practice Asits illustration of thewhole loading process it seems that thereare not toomany events that could be found in the figureThedetailed characteristics will be shown in the following part

5 Discussion

51 Spectrum Characteristics in Different Loading Stages Asstated above every test is divided into three stages and tensections To analyze the characteristics of different materialsin different loading stages typical microseismic events fromeach stage are selected Figure 6 illustrates diagrammatically

Shock and Vibration 7

100 200 3000Time (s)

minus005

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005

PF-5

(mV

)

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02

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(mV

)

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(mV

)times10

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times10minus3

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0

05

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plitu

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0

5

Am

plitu

de

Figure 5 A typical LMD result of CSC2

sections of typicalMS events data of cement sample (CMSA1)and glass sample (GSB1) in early stage middle stage andpoststage In terms of cement specimen the maximumamplitude of signal increases obviously with the loadingstage the real amplitude from 1mV in early stage to 20mVin the poststage and relative amplitude of FFT from 001 to004 From the frequency domain point of view before thecoming of main fracture in poststage the frequency of signalis mainly from 0 to 500Hz with maximum frequency around100ndash500Hz while in the poststage the main frequency isfrom 1000 to 1500Hz Also it should be noticed that in

every stage theMS signals all owned relativelywide frequencydomain distribution Analysis result of the same method oncoal specimen is very similar to cement However for glasssamples the maximum amplitude is not in the poststageand the frequency distribution is also different As shown inFigure 4 the MS events are detected in the whole loadingprocess Theoretically speaking coal and cement sampleswere similarly made up of powders and couplant to someextent these two kinds of samples are homogeneous andisotropic while glass samples are combination of severallayers This yields the difference of inter- or intragrain failure

8 Shock and Vibration

Original data (GSB1) Fast Fourier transform (GSB1)

(a)(a)

(b)

(c)(c)

(b)

Original data (CMSA1) Fast Fourier transform (CMSA1)

(a)

(b)

(c) (c)

(b)

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Figure 6 MS signal during cement and glass damage in different loading stages (a) early stage (b) middle stage (c) poststage

Shock and Vibration 9

CSA2CSB2CSC2CMSA2GSB1GSB3

CSB3CSB1

CMSA1CMSA3GSB2

Loading stage

100

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MS

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t acc

umul

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)

I II III IV V VI VII VIII IX X

Figure 7 The event accumulation variation during the loadingstage

and the rule of crack or cleat initiation and propagation inthe samples Furthermore the intrinsic resonant frequenciesof variations of materials also contribute to the monitoringresult

52MS Event Count and Event Rate Asmentioned above weused the STALTA method to recognize effective MS eventcounting the ring and event number During the detection ofeffective MS signal if the ratio of STA and LTA is over thethreshold the number counter of MS event and ring will betriggered but the difference is that a single event will not stopuntil the value goes back to being under the threshold whilethe ring number counts all single section over the criticalamplitude It can be seen from the study that the MS eventand ring count are quite different for these three kinds ofmaterials and this shows irregular characteristics even inthe same kind of material (Tables 2 and 3) An interestingphenomenon is that cement specimen procedures least MSevent in comparison with coal and glass However as theloading time for each sample is quite different the eventrate value is quite similar for all the samples The averageevent rate per second is around 2 but for cement sample likeCMSA1 and CMSA3 it is only 025 and 022

Figure 7 shows the total event accumulation ratio of thewhole loading process The variations tendencies are quitedifferent for different sample for one group of the samples(CSB2 CSA2 CSB3 GSB1 and GSB3) shows linear type ofincrease in the loading process while for the other seriesof samples (CMSA1 GSB2 CMSA2 and CMSA3) eventaccumulation increases smoothly at first (stages IndashV) andgrows sharply after that (stages VIndashX) These two types oftendencies give an illustration of MS activities in the whole

Specimen number

dc1dc2dc3dc4

dc5dc6dc7dc8

CSA

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00

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Ener

gy p

ropo

rtio

n (

)

Figure 8 Energy proportion of WPT best tree decomposition

process and this phenomenon has great agreement with thestress and material damage rules

53The Energy Distribution Characteristics of MS Signal Theanalysis on energy distribution ofMS signal is based on resultofWPT best tree transformation It can be seen from Figure 1that the signal is decomposed into 8 decompositions denotedas dc1 to dc8 According to the node number the corre-sponding frequency bands of each node component are 0ndash469Hz 469ndash938Hz 938ndash1406Hz 1406ndash1875Hz 1875ndash3750Hz 3750ndash750Hz 750ndash10781Hz and 10781ndash1500Hzrespectively Examples of several specimens in terms ofenergy distribution of the eight decompositions are shownin Figure 8 Hereby the energy distribution analysis alsoshows the frequency-spectrum characteristics of differentmaterials It is obvious to see and conclude that all specimensmade of different materials are abundant with low frequencycomponents especially for dc1 However some differencesbetween each material type could also be found In the casesof glass specimens the total signal energy for each specimenin 0ndash469Hz is all more than 50 (629 958 and 507)As glass is a kind of typical brittle material there are alwaysbig fractures generated directly when it is compressed anddeformed This kind of rapid release of energy will limitthe energy accumulation and the big fractures all limit thespreading of stress wave Consequently the signal energyis mainly from low frequency domain By contrast theenergy proportions for cement produced signals are widelydistributed in all frequency domains mainly covering the 0ndash938Hz and 1406ndash750Hz But for coal the situation is muchmore complicated The main energy distribution domain forevery sample seems to change greatly in their medium and

10 Shock and Vibration

Table 2 Ring count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X TotalCSA2 6367 7147 8068 5044 4966 4792 3178 4998 2793 11400 58753CSB1 194 1666 892 140 59 22 71 174 2399 4145 9762CSB2 1797 1938 1430 1264 1746 1117 814 1363 932 1570 13971CSB3 13174 6847 8510 8543 6849 7595 8019 10858 7705 9417 87517CSC2 1596 6451 3153 569 640 123 0 122 177 3259 16090CMSA1 630 57 43 0 905 567 238 686 707 1686 5519CMSA2 243 0 152 25 482 211 362 597 1146 2350 5568CMSA3 1652 325 199 175 667 1203 1563 3438 11684 11132 32038GSB1 6961 5387 7107 4334 3437 4303 3160 2680 3460 1507 42336GSB2 2016 2521 2578 2613 3802 6703 17618 23977 23964 21802 107594GSB3 11958 9097 10882 16121 11292 9896 10314 11962 18636 17162 127320

Table 3 MS event count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X Total Event rateCSA2 311 294 302 219 239 226 159 238 121 368 2477 236CSB1 23 57 27 4 2 1 3 6 84 118 325 217CSB2 94 111 93 78 101 71 61 81 67 77 834 309CSB3 559 409 428 472 359 405 413 519 388 478 4430 242CSC2 83 132 84 14 11 3 0 2 4 87 420 14CMSA1 10 0 6 2 4 6 17 21 36 81 183 022CMSA2 28 3 2 0 37 21 10 36 36 73 246 025CMSA3 66 34 20 21 27 67 66 108 374 333 1116 13GSB1 220 162 187 133 76 117 103 91 96 58 1243 143GSB2 100 92 107 90 176 268 531 609 602 603 3178 195GSB3 401 323 382 505 378 335 342 417 540 535 4158 258

high frequency components Specifically CSA2 has morefrequency components for d8 and CSB1 has more for d5while CSB3 is abundant with d7 This may be mainly becauseof the differences for loading rate and coal powder sizes andof course the boundaries and interfaces inside the samples

54 Relationship between MS Signal and Loading Rate Toanalyze the microseismic characteristics of different materi-als different loading rate is set in the process of experimentAs mentioned above coal cement paste and glass speci-mens have quite different mechanic strength and materialcharacteristics Thus we set different loading rate variationfor each type of materials for coal it is 5ndash20Ns and forcement and glass it is 150ndash300Ns Taking coal samples asan example the statistics on CSB1 CSB2 and CSB3 showthat the loading peak force values increase as the loading rateset larger and the bigger the loading rate the more the MSevents and ring number activities recodedThe figure for thisis 9762 and 13971 to 87517 in ring number and 325834 to 4430in MS single event Furthermore similar result for cementand glass sample can be found regarding the variation ofloading rate We believe that this is because MS phenomenonis actually kind of stress wave and release of energy and largerloading speed results in more rapid energy accumulating and

releasing outThus there aremoreMS events and ring countswere founded during the whole loading process

55 Relationship between MS Signal and Coal Powder ParticleSize A group of coal specimens (CSA2 CSB2 and CSC2)is selected to analyze the effect of coal powder size on MScharacteristics As shown in Table 1 under the same loadingrate (10Ns) the peak values are 774 kN 272 kN and 318 kNfor specimens made by particles size in 0ndash025mm 025ndash05mm and 05ndash2mm Theoretically the smaller the size ofpowders used is the larger the strength of pressured briquettesamples should be for small particles could bond wellAlthough CSB2 which was made up of 025ndash05mm particlesize powders only got 272 kN loading peak value before theprimary damage the general trend between CSA2 and CSC2showed an obvious decrease of sample strength with powdersize going up As bigger strength means more loading time itis not surprising thatmoreMS events were recorded for CSA2and CSC2 (Table 2) On the contrary CSB2 has the largestevent rate and it is 309 events every second as shown inTable 3 Another feature by which powder size will influencethe man-made specimen structure is the homogeneity Thisis because specimens made by larger particle size of powderhave more cracks inside As a result it will take longer time

Shock and Vibration 11

during the crack closure stage and finally get into linear elasticstage Also as shown in Figure 7 the curves of MS eventaccumulation with loading stage for CSA2 and CSB2 increasemore linearly while for CSC2 made by largest powder itis quite different In summary although the loading timespecimen strength and events statistic for CSB2 show someabnormality comparedwith general trend from small powdersize to large powder size made specimen the global eventaccumulation has great consistency with theoretical analysis

6 Conclusion

A total of 11 experiment tests on coal cement paste andglass specimens were carried out under uniaxial compressioncondition Coal powder particle size material type andloading rate are selected to compare and analyzeMSAE char-acteristic In the meantime the MS event rate accumulatedringing number relationship between loading stress and MSevent energy distribution and frequency-time characteris-tics of MS signal produced during the damage of differentspecimens were also discussed in the work

To separate the MS signal from the noises pollutedsignals the combination of wavelet packet transform (WPT)and local mean decomposition (LMD)methodwas chosen todenoise and analyze all the signals collected The applicationof this new method proved that the WPT-LMD method hada good performance in denoising and filtering the MS dataand it also could be used as common tool in the issues offrequency-spectrum characteristics analysis of MS signal orMS-like nonstationary and nonlinear signal

The investigation result on theMS signal features of threecategories of materials showed the following

(1) In terms of the damage and deformation of coal andcement in the early stage few effective signals areproduced then specimens will experience a relativeclam middle stage and in the poststage where mainrupture occurred a large number of MS events wererecorded But for glass samples theMS event genera-tions were throughout the whole loading process Weconclude that this is because of the brittle and crispyfeatures of glass material which means less energystored and quickly released

(2) Also there is good corresponding relation betweenloading time and MS events and ring numbers thelonger loading time is the more MS events and ringnumbers are detected while for loading rate there isno obvious effect on MS

(3) In the case of relationship for coal powder par-ticle size and MS feature we found that the MSaccumulation patterns of small particles (0ndash05mm)made specimens be more linear while large particles(05ndash2mm) made specimens own typical nonlinearcharacteristics The latter will spend much more timeto close the crack and be crushed into small powdersThere seems to be a decrease trend of specimenstrengths with the powder size used from small tolarge but there is abnormal case of CSB2 Similar

conclusion could be made for event and ring numbercounts and event rate this may be caused by a failureof specimen preparation and this is supposed to beexamined in the further study

(4) From the frequency-spectrum point of view all testson differentmaterials showedwide frequency domaincharacteristics which are fallen in the domain of0ndash200Hz 300ndash500Hz and 1000ndash1500Hz Howeverit should be noted that the frequency domain ofglass specimens is mainly concentrated in the lowfrequency (0ndash938Hz) due to their extremely brittlemechanical characteristics

To sum up we believe that the outcome of the paperwill be of great significance to improve the dynamic disastersforecast and improve coal mining safety This may embodysome promotions in data processing by using theWPT-LMDmethod and provide a theoretical basis for understandingthe mechanism of MS activities due to the experimentalphenomenon and conclusions presented

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This study was supported by National Natural Science Foun-dation of China (Grant no 51274206) The correspondingauthor also acknowledged the financial support provided byChinese Scholarship Council (CSC)

References

[1] C U Grosse andM Ohtsu Acoustic Emission Testing SpringerBerlin Germany 2008

[2] H RHardy JrAcoustic EmissionMicroseismic Activity Volume1 Principles Techniques and Geotechnical Applications CRCPress 2005

[3] C-P Lu L-M Dou B Liu Y-S Xie and H-S Liu ldquoMicroseis-mic low-frequency precursor effect of bursting failure of coaland rockrdquo Journal of Applied Geophysics vol 79 pp 55ndash63 2012

[4] C-P Lu L-M Dou H Liu H-S Liu B Liu and B-B DuldquoCase study on microseismic effect of coal and gas outburstprocessrdquo International Journal of Rock Mechanics and MiningSciences vol 53 pp 101ndash110 2012

[5] S I Qin J Cheng and S Zhu ldquoThe application and prospect ofmicroseismic technique in coalminerdquo Procedia EnvironmentalSciences vol 12 pp 218ndash224 2012

[6] X H Liu C X Liu S Zhuang L Wang and S S LinldquoLaboratory studies on acoustic emission characteristics to coaldynamic response under variable accelerative loadrdquo AppliedMechanics and Materials vol 580ndash583 pp 623ndash627 2014

[7] N Cook ldquoThe seismic location of rockburstsrdquo in Proceedings ofthe 5th Rock Mechanics Symposium pp 463ndash516 1963

[8] A G Beattie ldquoAcoustic emission principles and instrumenta-tionrdquo Journal of Acoustic Emission vol 2 no 12 pp 95ndash1281983

12 Shock and Vibration

[9] L Obert ldquoThe microseismic method discovery and earlyhistoryrdquo in Proceedings of the 1st Conference on Acoustic Emis-sionMicroseismic Activity in Geologic Structures and Materialspp 11ndash12 1977

[10] I Yamada K Masuda and H Mizutani ldquoElectromagnetic andacoustic emission associated with rock fracturerdquo Physics of theEarth and Planetary Interiors vol 57 no 1-2 pp 157ndash168 1989

[11] C-P Lu L-M Dou N Zhang et al ldquoMicroseismic frequency-spectrum evolutionary rule of rockburst triggered by roof fallrdquoInternational Journal of Rock Mechanics and Mining Sciencesvol 64 pp 6ndash16 2013

[12] S Sasaki ldquoCharacteristics of microseismic events inducedduring hydraulic fracturing experiments at the Hijiori hot dryrock geothermal energy site Yamagata Japanrdquo Tectonophysicsvol 289 no 1ndash3 pp 171ndash188 1998

[13] F Spaepen ldquoAmicroscopicmechanism for steady state inhomo-geneous flow in metallic glassesrdquo Acta Metallurgica vol 25 no4 pp 407ndash415 1977

[14] P E Tikkanen ldquoNonlinear wavelet and wavelet packet denois-ing of electrocardiogram signalrdquo Biological Cybernetics vol 80no 4 pp 259ndash267 1999

[15] O A M Aly A S Omar and A Z Elsherbeni ldquoDetectionand localization of RF radar pulses in noise environments usingwavelet packet transform and higher order statisticsrdquo Progressin Electromagnetics Research vol 58 pp 301ndash317 2006

[16] A Kyprianou P L Lewin V Efthimiou A Stavrou and GE Georghiou ldquoWavelet packet denoising for online partialdischarge detection in cables and its application to experimentalfield resultsrdquoMeasurement Science and Technology vol 17 no 9pp 2367ndash2379 2006

[17] J S Smith ldquoThe local mean decomposition and its applicationto EEG perception datardquo Journal of the Royal Society Interfacevol 2 no 5 pp 443ndash454 2005

[18] Z Sun and C C Chang ldquoStructural damage assessment basedon wavelet packet transformrdquo Journal of Structural Engineeringvol 128 no 10 pp 1354ndash1361 2002

[19] J-D Wu and C-H Liu ldquoAn expert system for fault diagnosisin internal combustion engines using wavelet packet transformand neural networkrdquo Expert Systems with Applications vol 36no 3 pp 4278ndash4286 2009

[20] Y Wang Z He and Y Zi ldquoA demodulation method based onimproved local mean decomposition and its application in rub-impact fault diagnosisrdquo Measurement Science and Technologyvol 20 no 2 Article ID 025704 2009

[21] Y Wang Z He and Y Zi ldquoA comparative study on thelocal mean decomposition and empirical mode decompositionand their applications to rotating machinery health diagnosisrdquoJournal of Vibration and Acoustics vol 132 no 2 Article ID021010 2010

[22] A Trnkoczy ldquoUnderstanding and parameter setting ofSTALTA trigger algorithmrdquo in IASPEI New Manual ofSeismological Observatory Practice vol 2 pp 1ndash19 DeutschesGeoForschungsZentrum GFZ Potsdam Germany 2002

[23] V Vishal P G Ranjith and T N Singh ldquoAn experimentalinvestigation on behaviour of coal under fluid saturationusing acoustic emissionrdquo Journal of Natural Gas Science andEngineering vol 22 pp 428ndash436 2015

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RotatingMachinery

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Submit your manuscripts athttpwwwhindawicom

VLSI Design

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Shock and Vibration

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Civil EngineeringAdvances in

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Chemical EngineeringInternational Journal of Antennas and

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International Journal of

2 Shock and Vibration

were also studied in the laboratory test scale [11 12] Howeverthe comparison of characteristics of MS events betweendifferent materials is merely considered and represented instudies above But this is very important for improvingthe accuracy of prealarming work underground This ismainly because the coal seam has great differences with itssurrounding rocks and due to the diversity of mechanicproperties the roof floor rocks and coal seam may generatequite different patterns of seismicity [3]

To study the MS characteristics of different materials wechoose the cement paste coal and glass to make specimensand perform some uniaxial compression loading on thesesamples and this kind of test was rarely found in the previousstudies There are several considerations for our decisions ofusing these threematerials Actually we selected themmainlyfor their distinguished mechanic properties Glass is selectedfor it is a type of typical brittle material it will be fractureddirectly under critical stress so here it is used to presentthe extremely brittle rocks [13] Coal is also kind of brittlerock but previous studies showed that coal would experiencea process of compaction before showing its linear elasticfeature and only after reaching the strain-soft stages did theprimary damage begin [3] The artificial briquette specimenwill be slightly different to natural coal bulks for it is morehomogeneous and isotropic but the main loading patternsare the same as natural bulks Cement paste specimens arewidely used in the research studies of construction and civilengineering issues the selection of cement paste means wehave more standard references and results to be comparedAlso in the underground field site there are many artificialconstructions with concrete and cement these nonnaturalstructures will also produce someMS signal whichmay affectthe accuracy of MS monitoring In summary we believe thatthese three kinds of material should be well represented fordifferent material properties

This paper mainly conducts experiment studies on mate-rial deformation damage and AEMS feature under uniaxialloading circumstance A new method by combining thewavelet packet transform (WPT) and local mean decompo-sition (LMD) is proposed to denoise and analyze MS signalOn the basis of the application of the WPT-LMD methodthe characteristics of MS signal produced by the deformationand damage of coal cement and glass samples are comparedand analyzed The remainder of this paper is summarizedas follows Section 2 describes the theory and applicationprocedure of WPT-LMD method In Section 3 the detailsof sample preparation and the procedure of laboratory testare presented In Section 4 the experiment results of severaltypical tests of different materials are illustrated Discussionsfor the effects of material type loading rate and coal particlesize onMS characteristics andMS event parameters statisticsare given in Section 5 Conclusion and future work arepresented in Section 6

2 MS Signal Processing Method

21 Combination of WPT and LMD A basic analysis onthe laboratory test data shows that the original signal is

partially swamped by the ambient noises Moreover thedominant frequency distribution range of MS signals herewas relatively wide both low frequency (lt100Hz) and highfrequency (500ndash1500Hz) compositions exist in the wholeeffective sample frequency domain Consequently it becomeshard to separate the real event signals from backgroundnoises by using sets of filters normally as used in the previousstudies such as some moving average filter Wiener filteror low-pass filter based on Fourier transform [14ndash17] Sowe developed a new method in this work which is actuallythe combination of wavelet packet transform (WPT) andlocal mean decomposition (LMD) This method is denotedas WPT-LMD method here and it is selected to performsignal denoising and analyzing throughout this work Theprinciple and algorithm of WPT have been widely discussedand presented in a lot of literatures [18 19] To be simpleonly the information of LMD is briefly described here but thedetails of its theory and algorithm could be found somewhereelse [17 20]

Local mean decomposition firstly proposed in 2005 isa new adaptive time-frequency analysis method [17] It isfamous for its good performances in dealing with nonlinearand nonstationary signals The basic algorithm of LMD isvery similar to empiricalmode decomposition (EMD) whichis developed earlier and widely applied in various studiesBut several investigations conclude that LMD is better thanEMD in signal processing especially for solving the endpointeffect and avoiding the mode mixing problems [20 21]LMD decomposes signal into a set of product functions(PFs) which represents characteristics of frequency band andenergy distribution [17 20] Dealing with the PFs it is easyto deny or filter the signal calculate instantaneous frequency(IF) and reconstruct reserved PFs into a new signal Basedon both the algorithms of WPT and LMD we successfullyactualized the data processing with WPT-LMDmethod andall coding works were conducted with MATLAB software

22 MS Signal Processing Procedure To start with WPTmethod is firstly adopted to conduct a soft threshold globaldenoising and then the denoised data are decomposed to findthe best tree structure (level 5) [19] The best tree nodes of acoal specimen (CSA2) are shown in Figure 1 To find the bestmother wavelet a denoising effect comparison among db5db8 and sym6 wavelet function is made and finally db8 ischosen

In the following process LMD method is performed onthe decomposition signal of each tree node and a seriesof PFs for every node are obtained Some PFs that mainlycontain environment noises will be determined as ineffectivedecomposition and removed while others will be judgedas effective PFs and reserved To determine whether a PFis an effective signal or noise decomposition the conceptof contribution coefficient is introduced The definition ofcontribution coefficient is shown in

119888119894=

1003816100381610038161003816119903119894

1003816100381610038161003816sdot 119890119894

sum119899

119894=1

1003816100381610038161003816119903119894

1003816100381610038161003816sdot 119890119894

(1)

Shock and Vibration 3

0

01

02

After WPT-LMD denoising

Original data (before denoising)

times10minus3

minus05

0

05

Am

plitu

de (m

V)

02 04 06 08 10Time (s)

Am

plitu

de

500 1000 15000Frequency (Hz)

minus05

0

05

Am

plitu

de (m

V)

02 04 06 08 10Time (s)

0

5

Am

plitu

de

500 1000 15000Frequency (Hz)

S

A approximate signalD detail signal

A1

A21 A22

A31

A41

A51 A52

D1

D21 D22

D31

D41

D51 D52

Figure 1 TheWPT best tree of CSA2 and denoising effect of WPT-LMDmethod

where 119888119894is referred to as the 119894th contribution coefficient 119894

is the number of PFs 119899 is the total number of PFs 119903119894is the

correlation coefficient between 119894th PF and original signal and119890119894is the occupation ratio of energy of 119894th PF only when 119888

119894

is larger than the critical value 0001 the PF is selected aseffective decomposition

All effective PFs preserved are combined to reconstructthe new tree nodes and then all nodes are reconstructed andgot the denoised signal series A comparison between theoriginal signal and denoised signal is also shown in Figure 1

Finally LMD method is used again on the denoisedsignal Some basic analysis could be easily done with the finalPFs such as the characteristics of frequency spectrum of thewhole signal or the features of the energy distribution andinstantaneous frequency of each PF

3 Experimental Setup

31 Experimental Equipment and Characteristic ParameterThe experimental testing system was comprised of stressloading equipment and data acquisition apparatus As shownin Figure 2 the EHF-UG500KN type servo press machine(Shimadzu Corp Japan) was used to provide the uni-axial compressive loading environment While the coaland rock dynamic disaster monitoring system (ZDKT-1CUMTB2009) was served as seismic activities monitoringfacilities and data acquisition module in this work ZDKT-1

is a real-time monitoring system developed for the forecastof coal and rock dynamic disasters in a coal mining field siteThis system is multichannel and multiparameter monitoringsystem and it can also be used in the laboratory test Elec-tromagnetic geomagnetic and microseismic transducersstress and strain gauges and gas concentration sensors areall included in the whole system It totally has 12 channelswith a 51 kHz maximum data sample rate In this work thesample rate was set as 3000Hz to digitize filter and storedata automatically

To describe characteristics of MS signal several basicparameters for both process parameters and state parametersare included in this work These parameters are ampli-tude frequency MS event counts and event rate energyaccumulation and energy release rate Actually all theseparameters are commonly used in the traditional MSAEstudies and their basic definition could be found somewhereelse [2] To get the values of these parameters the STALTA(shortlong term average) method was used to recognize theMS signal [22] Then every individual MS event detectedwas extracted from the total data and this will be used forfurther quantitative analysis on characteristic parametersWehave set the STA and LTA energy window lengths at 10msand 200ms respectively As the sample rate is 3000Hz theshort series number should be 30 and long series number is600 and the trigger threshold value for STA and LTA ratio isselected as 5

4 Shock and Vibration

(a) (b) (c)

Figure 2 Testing system (a) loading equipment (b) ZDKT-1 dynamic disaster monitoring system (c) data acquisition software

Figure 3 Original and damaged patterns of different types of specimens

32 Specimen Preparation Three types of specimens madeby different materials were tested in uniaxial compressionbriquette coal specimens cement specimens (pure Portlandcement) and glass specimens The original and damagedpatterns of all these three types of specimens are shown inFigure 3

Coal blocks were gathered from Pingdingshan 10 coalcolliery where strong dynamic disasters often occurredThese coal bulks are crushed and sieved into 05mmndash2mm025ndash05mm and less than 025mm particle powder indiameter After that some water and coal tar were mixedinto coal powder and the proportion by weight is 05 1 20(water coal tar coal powder) Then the mixed powder wascast in the steel molds and stressed under 10MPa axialpressure A small amount of wax was attached to the internalsurface of the mold before making every specimen to getout the shaped briquette sample easily To keep the goodmechanical consistency between these artificial samplesseveral measures were adopted (1) all specimens were madewith the same size mold (100 times 100 times 100) and the qualitiesof coal powder and coal tar were strictly controlled (2) Allspecimens were kept stressed under 10MPa axial loadingpressure for up to 8 hours (3)The specimens were removed

from the molds 24 hours after unloading the stress and curedfor 28 days in a curing room Totally there were 9 briquettespecimens made 3 for each particle size

In contrast two kinds of cement Portland 425 and425R types were selected to make cement specimens Thewhole process of making cement specimen is similar tothat of coal but the mix proportion for cement paste byweight is 035 1 (water cement) Also there was no externalloading conducted when making this kind of specimensThese specimens were removed from the mold after castingand cured for 28 days at 20 plusmn 2∘C Glass specimens weremade by gluing 20 layers of glass together with glass cementwith 5mm thickness for each piece Still for cement and glassspecimens there were 3 prepared for each type and they wereall in the same size of 100 times 100 times 100mm in cubic shape

33 Test Procedure The whole experiment process consistedof the following steps

(1) Place the specimens on the centre of the platform oftesting machine and attach microseismic transducerto the surface of specimen

Shock and Vibration 5

Table 1 Experimental parameters of specimens and work condition details

Specimen number Loading rate (Ns) Peak value (kN) Loading time (s) Special noteCSA2 10 774 1050 Particle size lt025mmCSB1 5 224 270 Particle size 025ndash050mmCSB2 10 272 150 Particle size 025ndash050mmCSB3 20 107 1830 Particle size 025ndash050mmCSC2 10 318 300 Particle size 050ndash200mmCMSA1 150 12322 910 Cement type PO 425CMSA2 200 16296 860 Cement type PO 425CMSA3 250 19659 820 Cement type PO 425GSB1 200 2963 1610 Layer count 10GSB2 250 20448 870 Layer count 10GSB3 300 4793 1630 Layer count 10

(2) Turn on the ZDKT-1 type dynamic disaster monitorapparatus start up the monitoring operation systemand set related initial parameters well for examplethe loading rate sample frequency and initial loadingforce

(3) Before recording the data verify that signal acquisi-tion module worked well and begin to record data

(4) Start the loading process and keep loading until thespecimens were completely damaged

(5) Stop loading and recording save the data file(6) Repeat the procedure again and run test on other

specimens

4 Experimental Result and Analysis

A series of uniaxial compression loading were conducted oncoal cement and glass specimensThe detailed experimentalparameters such as the loading rate loading last time andthe peak value of loading force are shown in Table 1 Dueto the space limitation only partial results of the tests foreach kind of specimen are presented in this paper To bemore specific they are actual 5 tests of coal specimens 3 testsof cement and 3 tests of glass But that is still enough toshow the characteristics changes of MS signal under variousloading conditions For instance CSB1 CSB2 and CSB3 areused for discussion on MS feature changes with the variationof the loading rate CSA2 CSB2 and CSC2 are selectedto give a comparison between samples made by differentparticle size of coal powders and of course MS data recordedfrom different materials are mainly used to compare thecharacteristics of MS events from each other

The material failure process is accompanied by MSAEwhich is actually a form of quick release of stored energySpecimens here were investigated for their mechanical per-formances with microseismicity patterns throughout thewhole loading process Theoretically speaking the continu-ous loading on material will cause the accumulation of strainenergy inside once the accumulated energy exceeds the ulti-mate value limited by the mechanical properties of materialsinternal fracture generation and external damage will occur

Generally there are five detailed stages of the damage anddeformation of materials under uniaxial compression thatis crack closure crack initiation secondary cracking crackcoalescence and crack damage [23] However the actualstress loading patterns will be affected a lot due to themechanical properties of materials The MS activities andloading stress variations with the loading time increase areillustrated in Figure 4 To make it easier to be analyzed eachtest was divided into 10 loading sections according to totalloading timeWe defined sections IndashIII as early stage sectionsIVndashVII as middle stage and sections VIIIndashX as poststage

In case of coal specimens MS activities occurred at bothearly stage and poststage but the primary fracture period ofcoal occurred in the poststage As illustrated in Figure 4 atearly loading stage of coal samples there were several MSactivities produced after that before the peak of loadingstress only few MS activities could be observed most ofthe MS events were collected after the loading peak valueThese briquette coal specimens mostly were destroyed understress less than 10KN and produce MS signals with lowestamplitude for 15mV in comparison with cement and glassspecimensThe patterns of MS events for cement samples arevery similar to coal most activities are collected in primaryfracture and early stage but it should be noticed in themiddleof loading just before the main fracture happens that theMS release is also very active With respect to event rate thevalues for cement samples are lowest in all three materialsFor the glass samples due to their larger uniaxial strengthand layer distribution more MS activities are found throughthewhole loading process Loading on glass samples obtainedthe largest loading stress and the highest MS amplituderanging from 250 kN to 300 kN and approximately 100mVrespectively the same value to cement specimen is rangingfrom 150 kN to 200 kN and 60mV

The LMD result of CSC2 is shown in Figure 5 Thedenoised signal which is obtained by filtering the originalsignal is decomposed into several PFs These PFs havedifferent characteristics in both frequency and time domainThe frequency distributions are separated into three seriesThe high frequency is all collected at the early stage andmainfracture stage with highest amplitude and the frequency isabout 1000ndash1200Hz The medium frequency is the second

6 Shock and Vibration

III

III

IV

V

VI

VIIVIII

IXX

CSA2MSF

CMSA1MSF

GSB1MSF

III

IIIIV

VVI

VIIVIII

IXX

III

IIIIV

VVI

VIIVIII

IXX

III

IIIIV

V

VIVII

VIIIIX

X

I IIIII

IVV

VI

VIIVIII

IXX

III

IIIIV

V VI

VIIVIII

IXX

minus15

minus10

minus5

0

5

10

15M

S (m

V)

200 400 600 800 1000 12000Time (s)

0

2

4

6

8

F (k

N)

minus20

minus15

minus10

minus5

0

5

10

15

20

MS

(mV

)

50 100 150 200 250 300 3500Time (s)

0

1

2

3

4

F (k

N)

CSC2MSF

CMSA2MSF

GSB2MSF

minus60

minus40

minus20

0

20

40

60

MS

(mV

)

150 300 450 600 750 9000Time (s)

0

50

100

150

200

F (k

N)

0

50

100

150

F (k

N)

200 400 600 800 10000Time (s)

minus60

minus40

minus20

0

20

40

60

MS

(mV

)

minus100

minus50

0

50

100

MS

(mV

)

200 400 600 800 1000 1200 1400 16000Time (s)

0

100

200

300

F (k

N)

150 300 450 600 750 9000Time (s)

0

50

100

150

200

250

F (k

N)

minus100

minus50

0

50

100

MS

(mV

)

Figure 4 The MS events signal with increase of loading stress and time

most abundant withMS events its frequency ranges from 100to 500Hz Low frequency decomposition owns low ampli-tude but it should not be overlooked for low frequency signal(lower than 100Hz) usually spread further in real practice Asits illustration of thewhole loading process it seems that thereare not toomany events that could be found in the figureThedetailed characteristics will be shown in the following part

5 Discussion

51 Spectrum Characteristics in Different Loading Stages Asstated above every test is divided into three stages and tensections To analyze the characteristics of different materialsin different loading stages typical microseismic events fromeach stage are selected Figure 6 illustrates diagrammatically

Shock and Vibration 7

100 200 3000Time (s)

minus005

0

005

PF-5

(mV

)

minus02

0

02

PF-4

(mV

)

100 200 3000Time (s)

100 200 3000Time (s)

minus05

0

05

PF-3

(mV

)

100 200 3000Time (s)

minus5

0

5

PF-2

(mV

)

minus10

0

10

PF-1

(mV

)

100 200 3000Time (s)

100 200 3000Time (s)

minus10

0

10

MS

(mV

)times10

minus3

0

1

2

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus3

0

1

2

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus4

500 1000 15000Frequency (Hz)

0

2

4

Am

plitu

de

times10minus4

0

05

1

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus4

500 1000 15000Frequency (Hz)

0

05

1

Am

plitu

de

times10minus5

500 1000 15000Frequency (Hz)

0

5

Am

plitu

de

Figure 5 A typical LMD result of CSC2

sections of typicalMS events data of cement sample (CMSA1)and glass sample (GSB1) in early stage middle stage andpoststage In terms of cement specimen the maximumamplitude of signal increases obviously with the loadingstage the real amplitude from 1mV in early stage to 20mVin the poststage and relative amplitude of FFT from 001 to004 From the frequency domain point of view before thecoming of main fracture in poststage the frequency of signalis mainly from 0 to 500Hz with maximum frequency around100ndash500Hz while in the poststage the main frequency isfrom 1000 to 1500Hz Also it should be noticed that in

every stage theMS signals all owned relativelywide frequencydomain distribution Analysis result of the same method oncoal specimen is very similar to cement However for glasssamples the maximum amplitude is not in the poststageand the frequency distribution is also different As shown inFigure 4 the MS events are detected in the whole loadingprocess Theoretically speaking coal and cement sampleswere similarly made up of powders and couplant to someextent these two kinds of samples are homogeneous andisotropic while glass samples are combination of severallayers This yields the difference of inter- or intragrain failure

8 Shock and Vibration

Original data (GSB1) Fast Fourier transform (GSB1)

(a)(a)

(b)

(c)(c)

(b)

Original data (CMSA1) Fast Fourier transform (CMSA1)

(a)

(b)

(c) (c)

(b)

(a)

minus1minus05

005

1

Am

plitu

de (m

V)

01 09 10 05 06 07 080302 04Time (s)

01 02 03 04 05 06 07 08 09 10Time (s)

minus1minus05

005

1

Am

plitu

de (m

V)

minus20minus10

01020

Am

plitu

de (m

V)

01 09 10 05 06 07 080302 04Time (s)

minus20

minus10

0

10

20

Am

plitu

de (m

V)

01 02 03 04 05 06 07 08 09 10Time (s)

01 02 03 04 05 06 07 08 09 10Time (s)

minus20

minus10

0

10

20

Am

plitu

de (m

V)

01 02 03 04 05 06 07 08 09 10Time (s)

minus5

0

5

Am

plitu

de (m

V)

500 1000 15000Frequency (Hz)

00002000400060008

001

Am

plitu

de

500 1000 15000Frequency (Hz)

0

005

01

015

02

Am

plitu

de

500 1000 15000Frequency (Hz)

0

01

02

03

04

Am

plitu

de

500 1000 15000Frequency (Hz)

0001002003004

Am

plitu

de

500 1000 15000Frequency (Hz)

00005

0010015

002

Am

plitu

de

500 1000 15000Frequency (Hz)

00002000400060008

001

Am

plitu

de

Figure 6 MS signal during cement and glass damage in different loading stages (a) early stage (b) middle stage (c) poststage

Shock and Vibration 9

CSA2CSB2CSC2CMSA2GSB1GSB3

CSB3CSB1

CMSA1CMSA3GSB2

Loading stage

100

80

60

40

20

0

MS

even

t acc

umul

atio

n (

)

I II III IV V VI VII VIII IX X

Figure 7 The event accumulation variation during the loadingstage

and the rule of crack or cleat initiation and propagation inthe samples Furthermore the intrinsic resonant frequenciesof variations of materials also contribute to the monitoringresult

52MS Event Count and Event Rate Asmentioned above weused the STALTA method to recognize effective MS eventcounting the ring and event number During the detection ofeffective MS signal if the ratio of STA and LTA is over thethreshold the number counter of MS event and ring will betriggered but the difference is that a single event will not stopuntil the value goes back to being under the threshold whilethe ring number counts all single section over the criticalamplitude It can be seen from the study that the MS eventand ring count are quite different for these three kinds ofmaterials and this shows irregular characteristics even inthe same kind of material (Tables 2 and 3) An interestingphenomenon is that cement specimen procedures least MSevent in comparison with coal and glass However as theloading time for each sample is quite different the eventrate value is quite similar for all the samples The averageevent rate per second is around 2 but for cement sample likeCMSA1 and CMSA3 it is only 025 and 022

Figure 7 shows the total event accumulation ratio of thewhole loading process The variations tendencies are quitedifferent for different sample for one group of the samples(CSB2 CSA2 CSB3 GSB1 and GSB3) shows linear type ofincrease in the loading process while for the other seriesof samples (CMSA1 GSB2 CMSA2 and CMSA3) eventaccumulation increases smoothly at first (stages IndashV) andgrows sharply after that (stages VIndashX) These two types oftendencies give an illustration of MS activities in the whole

Specimen number

dc1dc2dc3dc4

dc5dc6dc7dc8

CSA

2

CMSA

2

GSB

1

GSB

3

GSB

2

CSB3

CSB1

CMSA

1

CMSA

3

00

02

04

06

08

10

Ener

gy p

ropo

rtio

n (

)

Figure 8 Energy proportion of WPT best tree decomposition

process and this phenomenon has great agreement with thestress and material damage rules

53The Energy Distribution Characteristics of MS Signal Theanalysis on energy distribution ofMS signal is based on resultofWPT best tree transformation It can be seen from Figure 1that the signal is decomposed into 8 decompositions denotedas dc1 to dc8 According to the node number the corre-sponding frequency bands of each node component are 0ndash469Hz 469ndash938Hz 938ndash1406Hz 1406ndash1875Hz 1875ndash3750Hz 3750ndash750Hz 750ndash10781Hz and 10781ndash1500Hzrespectively Examples of several specimens in terms ofenergy distribution of the eight decompositions are shownin Figure 8 Hereby the energy distribution analysis alsoshows the frequency-spectrum characteristics of differentmaterials It is obvious to see and conclude that all specimensmade of different materials are abundant with low frequencycomponents especially for dc1 However some differencesbetween each material type could also be found In the casesof glass specimens the total signal energy for each specimenin 0ndash469Hz is all more than 50 (629 958 and 507)As glass is a kind of typical brittle material there are alwaysbig fractures generated directly when it is compressed anddeformed This kind of rapid release of energy will limitthe energy accumulation and the big fractures all limit thespreading of stress wave Consequently the signal energyis mainly from low frequency domain By contrast theenergy proportions for cement produced signals are widelydistributed in all frequency domains mainly covering the 0ndash938Hz and 1406ndash750Hz But for coal the situation is muchmore complicated The main energy distribution domain forevery sample seems to change greatly in their medium and

10 Shock and Vibration

Table 2 Ring count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X TotalCSA2 6367 7147 8068 5044 4966 4792 3178 4998 2793 11400 58753CSB1 194 1666 892 140 59 22 71 174 2399 4145 9762CSB2 1797 1938 1430 1264 1746 1117 814 1363 932 1570 13971CSB3 13174 6847 8510 8543 6849 7595 8019 10858 7705 9417 87517CSC2 1596 6451 3153 569 640 123 0 122 177 3259 16090CMSA1 630 57 43 0 905 567 238 686 707 1686 5519CMSA2 243 0 152 25 482 211 362 597 1146 2350 5568CMSA3 1652 325 199 175 667 1203 1563 3438 11684 11132 32038GSB1 6961 5387 7107 4334 3437 4303 3160 2680 3460 1507 42336GSB2 2016 2521 2578 2613 3802 6703 17618 23977 23964 21802 107594GSB3 11958 9097 10882 16121 11292 9896 10314 11962 18636 17162 127320

Table 3 MS event count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X Total Event rateCSA2 311 294 302 219 239 226 159 238 121 368 2477 236CSB1 23 57 27 4 2 1 3 6 84 118 325 217CSB2 94 111 93 78 101 71 61 81 67 77 834 309CSB3 559 409 428 472 359 405 413 519 388 478 4430 242CSC2 83 132 84 14 11 3 0 2 4 87 420 14CMSA1 10 0 6 2 4 6 17 21 36 81 183 022CMSA2 28 3 2 0 37 21 10 36 36 73 246 025CMSA3 66 34 20 21 27 67 66 108 374 333 1116 13GSB1 220 162 187 133 76 117 103 91 96 58 1243 143GSB2 100 92 107 90 176 268 531 609 602 603 3178 195GSB3 401 323 382 505 378 335 342 417 540 535 4158 258

high frequency components Specifically CSA2 has morefrequency components for d8 and CSB1 has more for d5while CSB3 is abundant with d7 This may be mainly becauseof the differences for loading rate and coal powder sizes andof course the boundaries and interfaces inside the samples

54 Relationship between MS Signal and Loading Rate Toanalyze the microseismic characteristics of different materi-als different loading rate is set in the process of experimentAs mentioned above coal cement paste and glass speci-mens have quite different mechanic strength and materialcharacteristics Thus we set different loading rate variationfor each type of materials for coal it is 5ndash20Ns and forcement and glass it is 150ndash300Ns Taking coal samples asan example the statistics on CSB1 CSB2 and CSB3 showthat the loading peak force values increase as the loading rateset larger and the bigger the loading rate the more the MSevents and ring number activities recodedThe figure for thisis 9762 and 13971 to 87517 in ring number and 325834 to 4430in MS single event Furthermore similar result for cementand glass sample can be found regarding the variation ofloading rate We believe that this is because MS phenomenonis actually kind of stress wave and release of energy and largerloading speed results in more rapid energy accumulating and

releasing outThus there aremoreMS events and ring countswere founded during the whole loading process

55 Relationship between MS Signal and Coal Powder ParticleSize A group of coal specimens (CSA2 CSB2 and CSC2)is selected to analyze the effect of coal powder size on MScharacteristics As shown in Table 1 under the same loadingrate (10Ns) the peak values are 774 kN 272 kN and 318 kNfor specimens made by particles size in 0ndash025mm 025ndash05mm and 05ndash2mm Theoretically the smaller the size ofpowders used is the larger the strength of pressured briquettesamples should be for small particles could bond wellAlthough CSB2 which was made up of 025ndash05mm particlesize powders only got 272 kN loading peak value before theprimary damage the general trend between CSA2 and CSC2showed an obvious decrease of sample strength with powdersize going up As bigger strength means more loading time itis not surprising thatmoreMS events were recorded for CSA2and CSC2 (Table 2) On the contrary CSB2 has the largestevent rate and it is 309 events every second as shown inTable 3 Another feature by which powder size will influencethe man-made specimen structure is the homogeneity Thisis because specimens made by larger particle size of powderhave more cracks inside As a result it will take longer time

Shock and Vibration 11

during the crack closure stage and finally get into linear elasticstage Also as shown in Figure 7 the curves of MS eventaccumulation with loading stage for CSA2 and CSB2 increasemore linearly while for CSC2 made by largest powder itis quite different In summary although the loading timespecimen strength and events statistic for CSB2 show someabnormality comparedwith general trend from small powdersize to large powder size made specimen the global eventaccumulation has great consistency with theoretical analysis

6 Conclusion

A total of 11 experiment tests on coal cement paste andglass specimens were carried out under uniaxial compressioncondition Coal powder particle size material type andloading rate are selected to compare and analyzeMSAE char-acteristic In the meantime the MS event rate accumulatedringing number relationship between loading stress and MSevent energy distribution and frequency-time characteris-tics of MS signal produced during the damage of differentspecimens were also discussed in the work

To separate the MS signal from the noises pollutedsignals the combination of wavelet packet transform (WPT)and local mean decomposition (LMD)methodwas chosen todenoise and analyze all the signals collected The applicationof this new method proved that the WPT-LMD method hada good performance in denoising and filtering the MS dataand it also could be used as common tool in the issues offrequency-spectrum characteristics analysis of MS signal orMS-like nonstationary and nonlinear signal

The investigation result on theMS signal features of threecategories of materials showed the following

(1) In terms of the damage and deformation of coal andcement in the early stage few effective signals areproduced then specimens will experience a relativeclam middle stage and in the poststage where mainrupture occurred a large number of MS events wererecorded But for glass samples theMS event genera-tions were throughout the whole loading process Weconclude that this is because of the brittle and crispyfeatures of glass material which means less energystored and quickly released

(2) Also there is good corresponding relation betweenloading time and MS events and ring numbers thelonger loading time is the more MS events and ringnumbers are detected while for loading rate there isno obvious effect on MS

(3) In the case of relationship for coal powder par-ticle size and MS feature we found that the MSaccumulation patterns of small particles (0ndash05mm)made specimens be more linear while large particles(05ndash2mm) made specimens own typical nonlinearcharacteristics The latter will spend much more timeto close the crack and be crushed into small powdersThere seems to be a decrease trend of specimenstrengths with the powder size used from small tolarge but there is abnormal case of CSB2 Similar

conclusion could be made for event and ring numbercounts and event rate this may be caused by a failureof specimen preparation and this is supposed to beexamined in the further study

(4) From the frequency-spectrum point of view all testson differentmaterials showedwide frequency domaincharacteristics which are fallen in the domain of0ndash200Hz 300ndash500Hz and 1000ndash1500Hz Howeverit should be noted that the frequency domain ofglass specimens is mainly concentrated in the lowfrequency (0ndash938Hz) due to their extremely brittlemechanical characteristics

To sum up we believe that the outcome of the paperwill be of great significance to improve the dynamic disastersforecast and improve coal mining safety This may embodysome promotions in data processing by using theWPT-LMDmethod and provide a theoretical basis for understandingthe mechanism of MS activities due to the experimentalphenomenon and conclusions presented

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This study was supported by National Natural Science Foun-dation of China (Grant no 51274206) The correspondingauthor also acknowledged the financial support provided byChinese Scholarship Council (CSC)

References

[1] C U Grosse andM Ohtsu Acoustic Emission Testing SpringerBerlin Germany 2008

[2] H RHardy JrAcoustic EmissionMicroseismic Activity Volume1 Principles Techniques and Geotechnical Applications CRCPress 2005

[3] C-P Lu L-M Dou B Liu Y-S Xie and H-S Liu ldquoMicroseis-mic low-frequency precursor effect of bursting failure of coaland rockrdquo Journal of Applied Geophysics vol 79 pp 55ndash63 2012

[4] C-P Lu L-M Dou H Liu H-S Liu B Liu and B-B DuldquoCase study on microseismic effect of coal and gas outburstprocessrdquo International Journal of Rock Mechanics and MiningSciences vol 53 pp 101ndash110 2012

[5] S I Qin J Cheng and S Zhu ldquoThe application and prospect ofmicroseismic technique in coalminerdquo Procedia EnvironmentalSciences vol 12 pp 218ndash224 2012

[6] X H Liu C X Liu S Zhuang L Wang and S S LinldquoLaboratory studies on acoustic emission characteristics to coaldynamic response under variable accelerative loadrdquo AppliedMechanics and Materials vol 580ndash583 pp 623ndash627 2014

[7] N Cook ldquoThe seismic location of rockburstsrdquo in Proceedings ofthe 5th Rock Mechanics Symposium pp 463ndash516 1963

[8] A G Beattie ldquoAcoustic emission principles and instrumenta-tionrdquo Journal of Acoustic Emission vol 2 no 12 pp 95ndash1281983

12 Shock and Vibration

[9] L Obert ldquoThe microseismic method discovery and earlyhistoryrdquo in Proceedings of the 1st Conference on Acoustic Emis-sionMicroseismic Activity in Geologic Structures and Materialspp 11ndash12 1977

[10] I Yamada K Masuda and H Mizutani ldquoElectromagnetic andacoustic emission associated with rock fracturerdquo Physics of theEarth and Planetary Interiors vol 57 no 1-2 pp 157ndash168 1989

[11] C-P Lu L-M Dou N Zhang et al ldquoMicroseismic frequency-spectrum evolutionary rule of rockburst triggered by roof fallrdquoInternational Journal of Rock Mechanics and Mining Sciencesvol 64 pp 6ndash16 2013

[12] S Sasaki ldquoCharacteristics of microseismic events inducedduring hydraulic fracturing experiments at the Hijiori hot dryrock geothermal energy site Yamagata Japanrdquo Tectonophysicsvol 289 no 1ndash3 pp 171ndash188 1998

[13] F Spaepen ldquoAmicroscopicmechanism for steady state inhomo-geneous flow in metallic glassesrdquo Acta Metallurgica vol 25 no4 pp 407ndash415 1977

[14] P E Tikkanen ldquoNonlinear wavelet and wavelet packet denois-ing of electrocardiogram signalrdquo Biological Cybernetics vol 80no 4 pp 259ndash267 1999

[15] O A M Aly A S Omar and A Z Elsherbeni ldquoDetectionand localization of RF radar pulses in noise environments usingwavelet packet transform and higher order statisticsrdquo Progressin Electromagnetics Research vol 58 pp 301ndash317 2006

[16] A Kyprianou P L Lewin V Efthimiou A Stavrou and GE Georghiou ldquoWavelet packet denoising for online partialdischarge detection in cables and its application to experimentalfield resultsrdquoMeasurement Science and Technology vol 17 no 9pp 2367ndash2379 2006

[17] J S Smith ldquoThe local mean decomposition and its applicationto EEG perception datardquo Journal of the Royal Society Interfacevol 2 no 5 pp 443ndash454 2005

[18] Z Sun and C C Chang ldquoStructural damage assessment basedon wavelet packet transformrdquo Journal of Structural Engineeringvol 128 no 10 pp 1354ndash1361 2002

[19] J-D Wu and C-H Liu ldquoAn expert system for fault diagnosisin internal combustion engines using wavelet packet transformand neural networkrdquo Expert Systems with Applications vol 36no 3 pp 4278ndash4286 2009

[20] Y Wang Z He and Y Zi ldquoA demodulation method based onimproved local mean decomposition and its application in rub-impact fault diagnosisrdquo Measurement Science and Technologyvol 20 no 2 Article ID 025704 2009

[21] Y Wang Z He and Y Zi ldquoA comparative study on thelocal mean decomposition and empirical mode decompositionand their applications to rotating machinery health diagnosisrdquoJournal of Vibration and Acoustics vol 132 no 2 Article ID021010 2010

[22] A Trnkoczy ldquoUnderstanding and parameter setting ofSTALTA trigger algorithmrdquo in IASPEI New Manual ofSeismological Observatory Practice vol 2 pp 1ndash19 DeutschesGeoForschungsZentrum GFZ Potsdam Germany 2002

[23] V Vishal P G Ranjith and T N Singh ldquoAn experimentalinvestigation on behaviour of coal under fluid saturationusing acoustic emissionrdquo Journal of Natural Gas Science andEngineering vol 22 pp 428ndash436 2015

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International Journal of

Shock and Vibration 3

0

01

02

After WPT-LMD denoising

Original data (before denoising)

times10minus3

minus05

0

05

Am

plitu

de (m

V)

02 04 06 08 10Time (s)

Am

plitu

de

500 1000 15000Frequency (Hz)

minus05

0

05

Am

plitu

de (m

V)

02 04 06 08 10Time (s)

0

5

Am

plitu

de

500 1000 15000Frequency (Hz)

S

A approximate signalD detail signal

A1

A21 A22

A31

A41

A51 A52

D1

D21 D22

D31

D41

D51 D52

Figure 1 TheWPT best tree of CSA2 and denoising effect of WPT-LMDmethod

where 119888119894is referred to as the 119894th contribution coefficient 119894

is the number of PFs 119899 is the total number of PFs 119903119894is the

correlation coefficient between 119894th PF and original signal and119890119894is the occupation ratio of energy of 119894th PF only when 119888

119894

is larger than the critical value 0001 the PF is selected aseffective decomposition

All effective PFs preserved are combined to reconstructthe new tree nodes and then all nodes are reconstructed andgot the denoised signal series A comparison between theoriginal signal and denoised signal is also shown in Figure 1

Finally LMD method is used again on the denoisedsignal Some basic analysis could be easily done with the finalPFs such as the characteristics of frequency spectrum of thewhole signal or the features of the energy distribution andinstantaneous frequency of each PF

3 Experimental Setup

31 Experimental Equipment and Characteristic ParameterThe experimental testing system was comprised of stressloading equipment and data acquisition apparatus As shownin Figure 2 the EHF-UG500KN type servo press machine(Shimadzu Corp Japan) was used to provide the uni-axial compressive loading environment While the coaland rock dynamic disaster monitoring system (ZDKT-1CUMTB2009) was served as seismic activities monitoringfacilities and data acquisition module in this work ZDKT-1

is a real-time monitoring system developed for the forecastof coal and rock dynamic disasters in a coal mining field siteThis system is multichannel and multiparameter monitoringsystem and it can also be used in the laboratory test Elec-tromagnetic geomagnetic and microseismic transducersstress and strain gauges and gas concentration sensors areall included in the whole system It totally has 12 channelswith a 51 kHz maximum data sample rate In this work thesample rate was set as 3000Hz to digitize filter and storedata automatically

To describe characteristics of MS signal several basicparameters for both process parameters and state parametersare included in this work These parameters are ampli-tude frequency MS event counts and event rate energyaccumulation and energy release rate Actually all theseparameters are commonly used in the traditional MSAEstudies and their basic definition could be found somewhereelse [2] To get the values of these parameters the STALTA(shortlong term average) method was used to recognize theMS signal [22] Then every individual MS event detectedwas extracted from the total data and this will be used forfurther quantitative analysis on characteristic parametersWehave set the STA and LTA energy window lengths at 10msand 200ms respectively As the sample rate is 3000Hz theshort series number should be 30 and long series number is600 and the trigger threshold value for STA and LTA ratio isselected as 5

4 Shock and Vibration

(a) (b) (c)

Figure 2 Testing system (a) loading equipment (b) ZDKT-1 dynamic disaster monitoring system (c) data acquisition software

Figure 3 Original and damaged patterns of different types of specimens

32 Specimen Preparation Three types of specimens madeby different materials were tested in uniaxial compressionbriquette coal specimens cement specimens (pure Portlandcement) and glass specimens The original and damagedpatterns of all these three types of specimens are shown inFigure 3

Coal blocks were gathered from Pingdingshan 10 coalcolliery where strong dynamic disasters often occurredThese coal bulks are crushed and sieved into 05mmndash2mm025ndash05mm and less than 025mm particle powder indiameter After that some water and coal tar were mixedinto coal powder and the proportion by weight is 05 1 20(water coal tar coal powder) Then the mixed powder wascast in the steel molds and stressed under 10MPa axialpressure A small amount of wax was attached to the internalsurface of the mold before making every specimen to getout the shaped briquette sample easily To keep the goodmechanical consistency between these artificial samplesseveral measures were adopted (1) all specimens were madewith the same size mold (100 times 100 times 100) and the qualitiesof coal powder and coal tar were strictly controlled (2) Allspecimens were kept stressed under 10MPa axial loadingpressure for up to 8 hours (3)The specimens were removed

from the molds 24 hours after unloading the stress and curedfor 28 days in a curing room Totally there were 9 briquettespecimens made 3 for each particle size

In contrast two kinds of cement Portland 425 and425R types were selected to make cement specimens Thewhole process of making cement specimen is similar tothat of coal but the mix proportion for cement paste byweight is 035 1 (water cement) Also there was no externalloading conducted when making this kind of specimensThese specimens were removed from the mold after castingand cured for 28 days at 20 plusmn 2∘C Glass specimens weremade by gluing 20 layers of glass together with glass cementwith 5mm thickness for each piece Still for cement and glassspecimens there were 3 prepared for each type and they wereall in the same size of 100 times 100 times 100mm in cubic shape

33 Test Procedure The whole experiment process consistedof the following steps

(1) Place the specimens on the centre of the platform oftesting machine and attach microseismic transducerto the surface of specimen

Shock and Vibration 5

Table 1 Experimental parameters of specimens and work condition details

Specimen number Loading rate (Ns) Peak value (kN) Loading time (s) Special noteCSA2 10 774 1050 Particle size lt025mmCSB1 5 224 270 Particle size 025ndash050mmCSB2 10 272 150 Particle size 025ndash050mmCSB3 20 107 1830 Particle size 025ndash050mmCSC2 10 318 300 Particle size 050ndash200mmCMSA1 150 12322 910 Cement type PO 425CMSA2 200 16296 860 Cement type PO 425CMSA3 250 19659 820 Cement type PO 425GSB1 200 2963 1610 Layer count 10GSB2 250 20448 870 Layer count 10GSB3 300 4793 1630 Layer count 10

(2) Turn on the ZDKT-1 type dynamic disaster monitorapparatus start up the monitoring operation systemand set related initial parameters well for examplethe loading rate sample frequency and initial loadingforce

(3) Before recording the data verify that signal acquisi-tion module worked well and begin to record data

(4) Start the loading process and keep loading until thespecimens were completely damaged

(5) Stop loading and recording save the data file(6) Repeat the procedure again and run test on other

specimens

4 Experimental Result and Analysis

A series of uniaxial compression loading were conducted oncoal cement and glass specimensThe detailed experimentalparameters such as the loading rate loading last time andthe peak value of loading force are shown in Table 1 Dueto the space limitation only partial results of the tests foreach kind of specimen are presented in this paper To bemore specific they are actual 5 tests of coal specimens 3 testsof cement and 3 tests of glass But that is still enough toshow the characteristics changes of MS signal under variousloading conditions For instance CSB1 CSB2 and CSB3 areused for discussion on MS feature changes with the variationof the loading rate CSA2 CSB2 and CSC2 are selectedto give a comparison between samples made by differentparticle size of coal powders and of course MS data recordedfrom different materials are mainly used to compare thecharacteristics of MS events from each other

The material failure process is accompanied by MSAEwhich is actually a form of quick release of stored energySpecimens here were investigated for their mechanical per-formances with microseismicity patterns throughout thewhole loading process Theoretically speaking the continu-ous loading on material will cause the accumulation of strainenergy inside once the accumulated energy exceeds the ulti-mate value limited by the mechanical properties of materialsinternal fracture generation and external damage will occur

Generally there are five detailed stages of the damage anddeformation of materials under uniaxial compression thatis crack closure crack initiation secondary cracking crackcoalescence and crack damage [23] However the actualstress loading patterns will be affected a lot due to themechanical properties of materials The MS activities andloading stress variations with the loading time increase areillustrated in Figure 4 To make it easier to be analyzed eachtest was divided into 10 loading sections according to totalloading timeWe defined sections IndashIII as early stage sectionsIVndashVII as middle stage and sections VIIIndashX as poststage

In case of coal specimens MS activities occurred at bothearly stage and poststage but the primary fracture period ofcoal occurred in the poststage As illustrated in Figure 4 atearly loading stage of coal samples there were several MSactivities produced after that before the peak of loadingstress only few MS activities could be observed most ofthe MS events were collected after the loading peak valueThese briquette coal specimens mostly were destroyed understress less than 10KN and produce MS signals with lowestamplitude for 15mV in comparison with cement and glassspecimensThe patterns of MS events for cement samples arevery similar to coal most activities are collected in primaryfracture and early stage but it should be noticed in themiddleof loading just before the main fracture happens that theMS release is also very active With respect to event rate thevalues for cement samples are lowest in all three materialsFor the glass samples due to their larger uniaxial strengthand layer distribution more MS activities are found throughthewhole loading process Loading on glass samples obtainedthe largest loading stress and the highest MS amplituderanging from 250 kN to 300 kN and approximately 100mVrespectively the same value to cement specimen is rangingfrom 150 kN to 200 kN and 60mV

The LMD result of CSC2 is shown in Figure 5 Thedenoised signal which is obtained by filtering the originalsignal is decomposed into several PFs These PFs havedifferent characteristics in both frequency and time domainThe frequency distributions are separated into three seriesThe high frequency is all collected at the early stage andmainfracture stage with highest amplitude and the frequency isabout 1000ndash1200Hz The medium frequency is the second

6 Shock and Vibration

III

III

IV

V

VI

VIIVIII

IXX

CSA2MSF

CMSA1MSF

GSB1MSF

III

IIIIV

VVI

VIIVIII

IXX

III

IIIIV

VVI

VIIVIII

IXX

III

IIIIV

V

VIVII

VIIIIX

X

I IIIII

IVV

VI

VIIVIII

IXX

III

IIIIV

V VI

VIIVIII

IXX

minus15

minus10

minus5

0

5

10

15M

S (m

V)

200 400 600 800 1000 12000Time (s)

0

2

4

6

8

F (k

N)

minus20

minus15

minus10

minus5

0

5

10

15

20

MS

(mV

)

50 100 150 200 250 300 3500Time (s)

0

1

2

3

4

F (k

N)

CSC2MSF

CMSA2MSF

GSB2MSF

minus60

minus40

minus20

0

20

40

60

MS

(mV

)

150 300 450 600 750 9000Time (s)

0

50

100

150

200

F (k

N)

0

50

100

150

F (k

N)

200 400 600 800 10000Time (s)

minus60

minus40

minus20

0

20

40

60

MS

(mV

)

minus100

minus50

0

50

100

MS

(mV

)

200 400 600 800 1000 1200 1400 16000Time (s)

0

100

200

300

F (k

N)

150 300 450 600 750 9000Time (s)

0

50

100

150

200

250

F (k

N)

minus100

minus50

0

50

100

MS

(mV

)

Figure 4 The MS events signal with increase of loading stress and time

most abundant withMS events its frequency ranges from 100to 500Hz Low frequency decomposition owns low ampli-tude but it should not be overlooked for low frequency signal(lower than 100Hz) usually spread further in real practice Asits illustration of thewhole loading process it seems that thereare not toomany events that could be found in the figureThedetailed characteristics will be shown in the following part

5 Discussion

51 Spectrum Characteristics in Different Loading Stages Asstated above every test is divided into three stages and tensections To analyze the characteristics of different materialsin different loading stages typical microseismic events fromeach stage are selected Figure 6 illustrates diagrammatically

Shock and Vibration 7

100 200 3000Time (s)

minus005

0

005

PF-5

(mV

)

minus02

0

02

PF-4

(mV

)

100 200 3000Time (s)

100 200 3000Time (s)

minus05

0

05

PF-3

(mV

)

100 200 3000Time (s)

minus5

0

5

PF-2

(mV

)

minus10

0

10

PF-1

(mV

)

100 200 3000Time (s)

100 200 3000Time (s)

minus10

0

10

MS

(mV

)times10

minus3

0

1

2

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus3

0

1

2

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus4

500 1000 15000Frequency (Hz)

0

2

4

Am

plitu

de

times10minus4

0

05

1

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus4

500 1000 15000Frequency (Hz)

0

05

1

Am

plitu

de

times10minus5

500 1000 15000Frequency (Hz)

0

5

Am

plitu

de

Figure 5 A typical LMD result of CSC2

sections of typicalMS events data of cement sample (CMSA1)and glass sample (GSB1) in early stage middle stage andpoststage In terms of cement specimen the maximumamplitude of signal increases obviously with the loadingstage the real amplitude from 1mV in early stage to 20mVin the poststage and relative amplitude of FFT from 001 to004 From the frequency domain point of view before thecoming of main fracture in poststage the frequency of signalis mainly from 0 to 500Hz with maximum frequency around100ndash500Hz while in the poststage the main frequency isfrom 1000 to 1500Hz Also it should be noticed that in

every stage theMS signals all owned relativelywide frequencydomain distribution Analysis result of the same method oncoal specimen is very similar to cement However for glasssamples the maximum amplitude is not in the poststageand the frequency distribution is also different As shown inFigure 4 the MS events are detected in the whole loadingprocess Theoretically speaking coal and cement sampleswere similarly made up of powders and couplant to someextent these two kinds of samples are homogeneous andisotropic while glass samples are combination of severallayers This yields the difference of inter- or intragrain failure

8 Shock and Vibration

Original data (GSB1) Fast Fourier transform (GSB1)

(a)(a)

(b)

(c)(c)

(b)

Original data (CMSA1) Fast Fourier transform (CMSA1)

(a)

(b)

(c) (c)

(b)

(a)

minus1minus05

005

1

Am

plitu

de (m

V)

01 09 10 05 06 07 080302 04Time (s)

01 02 03 04 05 06 07 08 09 10Time (s)

minus1minus05

005

1

Am

plitu

de (m

V)

minus20minus10

01020

Am

plitu

de (m

V)

01 09 10 05 06 07 080302 04Time (s)

minus20

minus10

0

10

20

Am

plitu

de (m

V)

01 02 03 04 05 06 07 08 09 10Time (s)

01 02 03 04 05 06 07 08 09 10Time (s)

minus20

minus10

0

10

20

Am

plitu

de (m

V)

01 02 03 04 05 06 07 08 09 10Time (s)

minus5

0

5

Am

plitu

de (m

V)

500 1000 15000Frequency (Hz)

00002000400060008

001

Am

plitu

de

500 1000 15000Frequency (Hz)

0

005

01

015

02

Am

plitu

de

500 1000 15000Frequency (Hz)

0

01

02

03

04

Am

plitu

de

500 1000 15000Frequency (Hz)

0001002003004

Am

plitu

de

500 1000 15000Frequency (Hz)

00005

0010015

002

Am

plitu

de

500 1000 15000Frequency (Hz)

00002000400060008

001

Am

plitu

de

Figure 6 MS signal during cement and glass damage in different loading stages (a) early stage (b) middle stage (c) poststage

Shock and Vibration 9

CSA2CSB2CSC2CMSA2GSB1GSB3

CSB3CSB1

CMSA1CMSA3GSB2

Loading stage

100

80

60

40

20

0

MS

even

t acc

umul

atio

n (

)

I II III IV V VI VII VIII IX X

Figure 7 The event accumulation variation during the loadingstage

and the rule of crack or cleat initiation and propagation inthe samples Furthermore the intrinsic resonant frequenciesof variations of materials also contribute to the monitoringresult

52MS Event Count and Event Rate Asmentioned above weused the STALTA method to recognize effective MS eventcounting the ring and event number During the detection ofeffective MS signal if the ratio of STA and LTA is over thethreshold the number counter of MS event and ring will betriggered but the difference is that a single event will not stopuntil the value goes back to being under the threshold whilethe ring number counts all single section over the criticalamplitude It can be seen from the study that the MS eventand ring count are quite different for these three kinds ofmaterials and this shows irregular characteristics even inthe same kind of material (Tables 2 and 3) An interestingphenomenon is that cement specimen procedures least MSevent in comparison with coal and glass However as theloading time for each sample is quite different the eventrate value is quite similar for all the samples The averageevent rate per second is around 2 but for cement sample likeCMSA1 and CMSA3 it is only 025 and 022

Figure 7 shows the total event accumulation ratio of thewhole loading process The variations tendencies are quitedifferent for different sample for one group of the samples(CSB2 CSA2 CSB3 GSB1 and GSB3) shows linear type ofincrease in the loading process while for the other seriesof samples (CMSA1 GSB2 CMSA2 and CMSA3) eventaccumulation increases smoothly at first (stages IndashV) andgrows sharply after that (stages VIndashX) These two types oftendencies give an illustration of MS activities in the whole

Specimen number

dc1dc2dc3dc4

dc5dc6dc7dc8

CSA

2

CMSA

2

GSB

1

GSB

3

GSB

2

CSB3

CSB1

CMSA

1

CMSA

3

00

02

04

06

08

10

Ener

gy p

ropo

rtio

n (

)

Figure 8 Energy proportion of WPT best tree decomposition

process and this phenomenon has great agreement with thestress and material damage rules

53The Energy Distribution Characteristics of MS Signal Theanalysis on energy distribution ofMS signal is based on resultofWPT best tree transformation It can be seen from Figure 1that the signal is decomposed into 8 decompositions denotedas dc1 to dc8 According to the node number the corre-sponding frequency bands of each node component are 0ndash469Hz 469ndash938Hz 938ndash1406Hz 1406ndash1875Hz 1875ndash3750Hz 3750ndash750Hz 750ndash10781Hz and 10781ndash1500Hzrespectively Examples of several specimens in terms ofenergy distribution of the eight decompositions are shownin Figure 8 Hereby the energy distribution analysis alsoshows the frequency-spectrum characteristics of differentmaterials It is obvious to see and conclude that all specimensmade of different materials are abundant with low frequencycomponents especially for dc1 However some differencesbetween each material type could also be found In the casesof glass specimens the total signal energy for each specimenin 0ndash469Hz is all more than 50 (629 958 and 507)As glass is a kind of typical brittle material there are alwaysbig fractures generated directly when it is compressed anddeformed This kind of rapid release of energy will limitthe energy accumulation and the big fractures all limit thespreading of stress wave Consequently the signal energyis mainly from low frequency domain By contrast theenergy proportions for cement produced signals are widelydistributed in all frequency domains mainly covering the 0ndash938Hz and 1406ndash750Hz But for coal the situation is muchmore complicated The main energy distribution domain forevery sample seems to change greatly in their medium and

10 Shock and Vibration

Table 2 Ring count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X TotalCSA2 6367 7147 8068 5044 4966 4792 3178 4998 2793 11400 58753CSB1 194 1666 892 140 59 22 71 174 2399 4145 9762CSB2 1797 1938 1430 1264 1746 1117 814 1363 932 1570 13971CSB3 13174 6847 8510 8543 6849 7595 8019 10858 7705 9417 87517CSC2 1596 6451 3153 569 640 123 0 122 177 3259 16090CMSA1 630 57 43 0 905 567 238 686 707 1686 5519CMSA2 243 0 152 25 482 211 362 597 1146 2350 5568CMSA3 1652 325 199 175 667 1203 1563 3438 11684 11132 32038GSB1 6961 5387 7107 4334 3437 4303 3160 2680 3460 1507 42336GSB2 2016 2521 2578 2613 3802 6703 17618 23977 23964 21802 107594GSB3 11958 9097 10882 16121 11292 9896 10314 11962 18636 17162 127320

Table 3 MS event count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X Total Event rateCSA2 311 294 302 219 239 226 159 238 121 368 2477 236CSB1 23 57 27 4 2 1 3 6 84 118 325 217CSB2 94 111 93 78 101 71 61 81 67 77 834 309CSB3 559 409 428 472 359 405 413 519 388 478 4430 242CSC2 83 132 84 14 11 3 0 2 4 87 420 14CMSA1 10 0 6 2 4 6 17 21 36 81 183 022CMSA2 28 3 2 0 37 21 10 36 36 73 246 025CMSA3 66 34 20 21 27 67 66 108 374 333 1116 13GSB1 220 162 187 133 76 117 103 91 96 58 1243 143GSB2 100 92 107 90 176 268 531 609 602 603 3178 195GSB3 401 323 382 505 378 335 342 417 540 535 4158 258

high frequency components Specifically CSA2 has morefrequency components for d8 and CSB1 has more for d5while CSB3 is abundant with d7 This may be mainly becauseof the differences for loading rate and coal powder sizes andof course the boundaries and interfaces inside the samples

54 Relationship between MS Signal and Loading Rate Toanalyze the microseismic characteristics of different materi-als different loading rate is set in the process of experimentAs mentioned above coal cement paste and glass speci-mens have quite different mechanic strength and materialcharacteristics Thus we set different loading rate variationfor each type of materials for coal it is 5ndash20Ns and forcement and glass it is 150ndash300Ns Taking coal samples asan example the statistics on CSB1 CSB2 and CSB3 showthat the loading peak force values increase as the loading rateset larger and the bigger the loading rate the more the MSevents and ring number activities recodedThe figure for thisis 9762 and 13971 to 87517 in ring number and 325834 to 4430in MS single event Furthermore similar result for cementand glass sample can be found regarding the variation ofloading rate We believe that this is because MS phenomenonis actually kind of stress wave and release of energy and largerloading speed results in more rapid energy accumulating and

releasing outThus there aremoreMS events and ring countswere founded during the whole loading process

55 Relationship between MS Signal and Coal Powder ParticleSize A group of coal specimens (CSA2 CSB2 and CSC2)is selected to analyze the effect of coal powder size on MScharacteristics As shown in Table 1 under the same loadingrate (10Ns) the peak values are 774 kN 272 kN and 318 kNfor specimens made by particles size in 0ndash025mm 025ndash05mm and 05ndash2mm Theoretically the smaller the size ofpowders used is the larger the strength of pressured briquettesamples should be for small particles could bond wellAlthough CSB2 which was made up of 025ndash05mm particlesize powders only got 272 kN loading peak value before theprimary damage the general trend between CSA2 and CSC2showed an obvious decrease of sample strength with powdersize going up As bigger strength means more loading time itis not surprising thatmoreMS events were recorded for CSA2and CSC2 (Table 2) On the contrary CSB2 has the largestevent rate and it is 309 events every second as shown inTable 3 Another feature by which powder size will influencethe man-made specimen structure is the homogeneity Thisis because specimens made by larger particle size of powderhave more cracks inside As a result it will take longer time

Shock and Vibration 11

during the crack closure stage and finally get into linear elasticstage Also as shown in Figure 7 the curves of MS eventaccumulation with loading stage for CSA2 and CSB2 increasemore linearly while for CSC2 made by largest powder itis quite different In summary although the loading timespecimen strength and events statistic for CSB2 show someabnormality comparedwith general trend from small powdersize to large powder size made specimen the global eventaccumulation has great consistency with theoretical analysis

6 Conclusion

A total of 11 experiment tests on coal cement paste andglass specimens were carried out under uniaxial compressioncondition Coal powder particle size material type andloading rate are selected to compare and analyzeMSAE char-acteristic In the meantime the MS event rate accumulatedringing number relationship between loading stress and MSevent energy distribution and frequency-time characteris-tics of MS signal produced during the damage of differentspecimens were also discussed in the work

To separate the MS signal from the noises pollutedsignals the combination of wavelet packet transform (WPT)and local mean decomposition (LMD)methodwas chosen todenoise and analyze all the signals collected The applicationof this new method proved that the WPT-LMD method hada good performance in denoising and filtering the MS dataand it also could be used as common tool in the issues offrequency-spectrum characteristics analysis of MS signal orMS-like nonstationary and nonlinear signal

The investigation result on theMS signal features of threecategories of materials showed the following

(1) In terms of the damage and deformation of coal andcement in the early stage few effective signals areproduced then specimens will experience a relativeclam middle stage and in the poststage where mainrupture occurred a large number of MS events wererecorded But for glass samples theMS event genera-tions were throughout the whole loading process Weconclude that this is because of the brittle and crispyfeatures of glass material which means less energystored and quickly released

(2) Also there is good corresponding relation betweenloading time and MS events and ring numbers thelonger loading time is the more MS events and ringnumbers are detected while for loading rate there isno obvious effect on MS

(3) In the case of relationship for coal powder par-ticle size and MS feature we found that the MSaccumulation patterns of small particles (0ndash05mm)made specimens be more linear while large particles(05ndash2mm) made specimens own typical nonlinearcharacteristics The latter will spend much more timeto close the crack and be crushed into small powdersThere seems to be a decrease trend of specimenstrengths with the powder size used from small tolarge but there is abnormal case of CSB2 Similar

conclusion could be made for event and ring numbercounts and event rate this may be caused by a failureof specimen preparation and this is supposed to beexamined in the further study

(4) From the frequency-spectrum point of view all testson differentmaterials showedwide frequency domaincharacteristics which are fallen in the domain of0ndash200Hz 300ndash500Hz and 1000ndash1500Hz Howeverit should be noted that the frequency domain ofglass specimens is mainly concentrated in the lowfrequency (0ndash938Hz) due to their extremely brittlemechanical characteristics

To sum up we believe that the outcome of the paperwill be of great significance to improve the dynamic disastersforecast and improve coal mining safety This may embodysome promotions in data processing by using theWPT-LMDmethod and provide a theoretical basis for understandingthe mechanism of MS activities due to the experimentalphenomenon and conclusions presented

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This study was supported by National Natural Science Foun-dation of China (Grant no 51274206) The correspondingauthor also acknowledged the financial support provided byChinese Scholarship Council (CSC)

References

[1] C U Grosse andM Ohtsu Acoustic Emission Testing SpringerBerlin Germany 2008

[2] H RHardy JrAcoustic EmissionMicroseismic Activity Volume1 Principles Techniques and Geotechnical Applications CRCPress 2005

[3] C-P Lu L-M Dou B Liu Y-S Xie and H-S Liu ldquoMicroseis-mic low-frequency precursor effect of bursting failure of coaland rockrdquo Journal of Applied Geophysics vol 79 pp 55ndash63 2012

[4] C-P Lu L-M Dou H Liu H-S Liu B Liu and B-B DuldquoCase study on microseismic effect of coal and gas outburstprocessrdquo International Journal of Rock Mechanics and MiningSciences vol 53 pp 101ndash110 2012

[5] S I Qin J Cheng and S Zhu ldquoThe application and prospect ofmicroseismic technique in coalminerdquo Procedia EnvironmentalSciences vol 12 pp 218ndash224 2012

[6] X H Liu C X Liu S Zhuang L Wang and S S LinldquoLaboratory studies on acoustic emission characteristics to coaldynamic response under variable accelerative loadrdquo AppliedMechanics and Materials vol 580ndash583 pp 623ndash627 2014

[7] N Cook ldquoThe seismic location of rockburstsrdquo in Proceedings ofthe 5th Rock Mechanics Symposium pp 463ndash516 1963

[8] A G Beattie ldquoAcoustic emission principles and instrumenta-tionrdquo Journal of Acoustic Emission vol 2 no 12 pp 95ndash1281983

12 Shock and Vibration

[9] L Obert ldquoThe microseismic method discovery and earlyhistoryrdquo in Proceedings of the 1st Conference on Acoustic Emis-sionMicroseismic Activity in Geologic Structures and Materialspp 11ndash12 1977

[10] I Yamada K Masuda and H Mizutani ldquoElectromagnetic andacoustic emission associated with rock fracturerdquo Physics of theEarth and Planetary Interiors vol 57 no 1-2 pp 157ndash168 1989

[11] C-P Lu L-M Dou N Zhang et al ldquoMicroseismic frequency-spectrum evolutionary rule of rockburst triggered by roof fallrdquoInternational Journal of Rock Mechanics and Mining Sciencesvol 64 pp 6ndash16 2013

[12] S Sasaki ldquoCharacteristics of microseismic events inducedduring hydraulic fracturing experiments at the Hijiori hot dryrock geothermal energy site Yamagata Japanrdquo Tectonophysicsvol 289 no 1ndash3 pp 171ndash188 1998

[13] F Spaepen ldquoAmicroscopicmechanism for steady state inhomo-geneous flow in metallic glassesrdquo Acta Metallurgica vol 25 no4 pp 407ndash415 1977

[14] P E Tikkanen ldquoNonlinear wavelet and wavelet packet denois-ing of electrocardiogram signalrdquo Biological Cybernetics vol 80no 4 pp 259ndash267 1999

[15] O A M Aly A S Omar and A Z Elsherbeni ldquoDetectionand localization of RF radar pulses in noise environments usingwavelet packet transform and higher order statisticsrdquo Progressin Electromagnetics Research vol 58 pp 301ndash317 2006

[16] A Kyprianou P L Lewin V Efthimiou A Stavrou and GE Georghiou ldquoWavelet packet denoising for online partialdischarge detection in cables and its application to experimentalfield resultsrdquoMeasurement Science and Technology vol 17 no 9pp 2367ndash2379 2006

[17] J S Smith ldquoThe local mean decomposition and its applicationto EEG perception datardquo Journal of the Royal Society Interfacevol 2 no 5 pp 443ndash454 2005

[18] Z Sun and C C Chang ldquoStructural damage assessment basedon wavelet packet transformrdquo Journal of Structural Engineeringvol 128 no 10 pp 1354ndash1361 2002

[19] J-D Wu and C-H Liu ldquoAn expert system for fault diagnosisin internal combustion engines using wavelet packet transformand neural networkrdquo Expert Systems with Applications vol 36no 3 pp 4278ndash4286 2009

[20] Y Wang Z He and Y Zi ldquoA demodulation method based onimproved local mean decomposition and its application in rub-impact fault diagnosisrdquo Measurement Science and Technologyvol 20 no 2 Article ID 025704 2009

[21] Y Wang Z He and Y Zi ldquoA comparative study on thelocal mean decomposition and empirical mode decompositionand their applications to rotating machinery health diagnosisrdquoJournal of Vibration and Acoustics vol 132 no 2 Article ID021010 2010

[22] A Trnkoczy ldquoUnderstanding and parameter setting ofSTALTA trigger algorithmrdquo in IASPEI New Manual ofSeismological Observatory Practice vol 2 pp 1ndash19 DeutschesGeoForschungsZentrum GFZ Potsdam Germany 2002

[23] V Vishal P G Ranjith and T N Singh ldquoAn experimentalinvestigation on behaviour of coal under fluid saturationusing acoustic emissionrdquo Journal of Natural Gas Science andEngineering vol 22 pp 428ndash436 2015

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4 Shock and Vibration

(a) (b) (c)

Figure 2 Testing system (a) loading equipment (b) ZDKT-1 dynamic disaster monitoring system (c) data acquisition software

Figure 3 Original and damaged patterns of different types of specimens

32 Specimen Preparation Three types of specimens madeby different materials were tested in uniaxial compressionbriquette coal specimens cement specimens (pure Portlandcement) and glass specimens The original and damagedpatterns of all these three types of specimens are shown inFigure 3

Coal blocks were gathered from Pingdingshan 10 coalcolliery where strong dynamic disasters often occurredThese coal bulks are crushed and sieved into 05mmndash2mm025ndash05mm and less than 025mm particle powder indiameter After that some water and coal tar were mixedinto coal powder and the proportion by weight is 05 1 20(water coal tar coal powder) Then the mixed powder wascast in the steel molds and stressed under 10MPa axialpressure A small amount of wax was attached to the internalsurface of the mold before making every specimen to getout the shaped briquette sample easily To keep the goodmechanical consistency between these artificial samplesseveral measures were adopted (1) all specimens were madewith the same size mold (100 times 100 times 100) and the qualitiesof coal powder and coal tar were strictly controlled (2) Allspecimens were kept stressed under 10MPa axial loadingpressure for up to 8 hours (3)The specimens were removed

from the molds 24 hours after unloading the stress and curedfor 28 days in a curing room Totally there were 9 briquettespecimens made 3 for each particle size

In contrast two kinds of cement Portland 425 and425R types were selected to make cement specimens Thewhole process of making cement specimen is similar tothat of coal but the mix proportion for cement paste byweight is 035 1 (water cement) Also there was no externalloading conducted when making this kind of specimensThese specimens were removed from the mold after castingand cured for 28 days at 20 plusmn 2∘C Glass specimens weremade by gluing 20 layers of glass together with glass cementwith 5mm thickness for each piece Still for cement and glassspecimens there were 3 prepared for each type and they wereall in the same size of 100 times 100 times 100mm in cubic shape

33 Test Procedure The whole experiment process consistedof the following steps

(1) Place the specimens on the centre of the platform oftesting machine and attach microseismic transducerto the surface of specimen

Shock and Vibration 5

Table 1 Experimental parameters of specimens and work condition details

Specimen number Loading rate (Ns) Peak value (kN) Loading time (s) Special noteCSA2 10 774 1050 Particle size lt025mmCSB1 5 224 270 Particle size 025ndash050mmCSB2 10 272 150 Particle size 025ndash050mmCSB3 20 107 1830 Particle size 025ndash050mmCSC2 10 318 300 Particle size 050ndash200mmCMSA1 150 12322 910 Cement type PO 425CMSA2 200 16296 860 Cement type PO 425CMSA3 250 19659 820 Cement type PO 425GSB1 200 2963 1610 Layer count 10GSB2 250 20448 870 Layer count 10GSB3 300 4793 1630 Layer count 10

(2) Turn on the ZDKT-1 type dynamic disaster monitorapparatus start up the monitoring operation systemand set related initial parameters well for examplethe loading rate sample frequency and initial loadingforce

(3) Before recording the data verify that signal acquisi-tion module worked well and begin to record data

(4) Start the loading process and keep loading until thespecimens were completely damaged

(5) Stop loading and recording save the data file(6) Repeat the procedure again and run test on other

specimens

4 Experimental Result and Analysis

A series of uniaxial compression loading were conducted oncoal cement and glass specimensThe detailed experimentalparameters such as the loading rate loading last time andthe peak value of loading force are shown in Table 1 Dueto the space limitation only partial results of the tests foreach kind of specimen are presented in this paper To bemore specific they are actual 5 tests of coal specimens 3 testsof cement and 3 tests of glass But that is still enough toshow the characteristics changes of MS signal under variousloading conditions For instance CSB1 CSB2 and CSB3 areused for discussion on MS feature changes with the variationof the loading rate CSA2 CSB2 and CSC2 are selectedto give a comparison between samples made by differentparticle size of coal powders and of course MS data recordedfrom different materials are mainly used to compare thecharacteristics of MS events from each other

The material failure process is accompanied by MSAEwhich is actually a form of quick release of stored energySpecimens here were investigated for their mechanical per-formances with microseismicity patterns throughout thewhole loading process Theoretically speaking the continu-ous loading on material will cause the accumulation of strainenergy inside once the accumulated energy exceeds the ulti-mate value limited by the mechanical properties of materialsinternal fracture generation and external damage will occur

Generally there are five detailed stages of the damage anddeformation of materials under uniaxial compression thatis crack closure crack initiation secondary cracking crackcoalescence and crack damage [23] However the actualstress loading patterns will be affected a lot due to themechanical properties of materials The MS activities andloading stress variations with the loading time increase areillustrated in Figure 4 To make it easier to be analyzed eachtest was divided into 10 loading sections according to totalloading timeWe defined sections IndashIII as early stage sectionsIVndashVII as middle stage and sections VIIIndashX as poststage

In case of coal specimens MS activities occurred at bothearly stage and poststage but the primary fracture period ofcoal occurred in the poststage As illustrated in Figure 4 atearly loading stage of coal samples there were several MSactivities produced after that before the peak of loadingstress only few MS activities could be observed most ofthe MS events were collected after the loading peak valueThese briquette coal specimens mostly were destroyed understress less than 10KN and produce MS signals with lowestamplitude for 15mV in comparison with cement and glassspecimensThe patterns of MS events for cement samples arevery similar to coal most activities are collected in primaryfracture and early stage but it should be noticed in themiddleof loading just before the main fracture happens that theMS release is also very active With respect to event rate thevalues for cement samples are lowest in all three materialsFor the glass samples due to their larger uniaxial strengthand layer distribution more MS activities are found throughthewhole loading process Loading on glass samples obtainedthe largest loading stress and the highest MS amplituderanging from 250 kN to 300 kN and approximately 100mVrespectively the same value to cement specimen is rangingfrom 150 kN to 200 kN and 60mV

The LMD result of CSC2 is shown in Figure 5 Thedenoised signal which is obtained by filtering the originalsignal is decomposed into several PFs These PFs havedifferent characteristics in both frequency and time domainThe frequency distributions are separated into three seriesThe high frequency is all collected at the early stage andmainfracture stage with highest amplitude and the frequency isabout 1000ndash1200Hz The medium frequency is the second

6 Shock and Vibration

III

III

IV

V

VI

VIIVIII

IXX

CSA2MSF

CMSA1MSF

GSB1MSF

III

IIIIV

VVI

VIIVIII

IXX

III

IIIIV

VVI

VIIVIII

IXX

III

IIIIV

V

VIVII

VIIIIX

X

I IIIII

IVV

VI

VIIVIII

IXX

III

IIIIV

V VI

VIIVIII

IXX

minus15

minus10

minus5

0

5

10

15M

S (m

V)

200 400 600 800 1000 12000Time (s)

0

2

4

6

8

F (k

N)

minus20

minus15

minus10

minus5

0

5

10

15

20

MS

(mV

)

50 100 150 200 250 300 3500Time (s)

0

1

2

3

4

F (k

N)

CSC2MSF

CMSA2MSF

GSB2MSF

minus60

minus40

minus20

0

20

40

60

MS

(mV

)

150 300 450 600 750 9000Time (s)

0

50

100

150

200

F (k

N)

0

50

100

150

F (k

N)

200 400 600 800 10000Time (s)

minus60

minus40

minus20

0

20

40

60

MS

(mV

)

minus100

minus50

0

50

100

MS

(mV

)

200 400 600 800 1000 1200 1400 16000Time (s)

0

100

200

300

F (k

N)

150 300 450 600 750 9000Time (s)

0

50

100

150

200

250

F (k

N)

minus100

minus50

0

50

100

MS

(mV

)

Figure 4 The MS events signal with increase of loading stress and time

most abundant withMS events its frequency ranges from 100to 500Hz Low frequency decomposition owns low ampli-tude but it should not be overlooked for low frequency signal(lower than 100Hz) usually spread further in real practice Asits illustration of thewhole loading process it seems that thereare not toomany events that could be found in the figureThedetailed characteristics will be shown in the following part

5 Discussion

51 Spectrum Characteristics in Different Loading Stages Asstated above every test is divided into three stages and tensections To analyze the characteristics of different materialsin different loading stages typical microseismic events fromeach stage are selected Figure 6 illustrates diagrammatically

Shock and Vibration 7

100 200 3000Time (s)

minus005

0

005

PF-5

(mV

)

minus02

0

02

PF-4

(mV

)

100 200 3000Time (s)

100 200 3000Time (s)

minus05

0

05

PF-3

(mV

)

100 200 3000Time (s)

minus5

0

5

PF-2

(mV

)

minus10

0

10

PF-1

(mV

)

100 200 3000Time (s)

100 200 3000Time (s)

minus10

0

10

MS

(mV

)times10

minus3

0

1

2

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus3

0

1

2

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus4

500 1000 15000Frequency (Hz)

0

2

4

Am

plitu

de

times10minus4

0

05

1

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus4

500 1000 15000Frequency (Hz)

0

05

1

Am

plitu

de

times10minus5

500 1000 15000Frequency (Hz)

0

5

Am

plitu

de

Figure 5 A typical LMD result of CSC2

sections of typicalMS events data of cement sample (CMSA1)and glass sample (GSB1) in early stage middle stage andpoststage In terms of cement specimen the maximumamplitude of signal increases obviously with the loadingstage the real amplitude from 1mV in early stage to 20mVin the poststage and relative amplitude of FFT from 001 to004 From the frequency domain point of view before thecoming of main fracture in poststage the frequency of signalis mainly from 0 to 500Hz with maximum frequency around100ndash500Hz while in the poststage the main frequency isfrom 1000 to 1500Hz Also it should be noticed that in

every stage theMS signals all owned relativelywide frequencydomain distribution Analysis result of the same method oncoal specimen is very similar to cement However for glasssamples the maximum amplitude is not in the poststageand the frequency distribution is also different As shown inFigure 4 the MS events are detected in the whole loadingprocess Theoretically speaking coal and cement sampleswere similarly made up of powders and couplant to someextent these two kinds of samples are homogeneous andisotropic while glass samples are combination of severallayers This yields the difference of inter- or intragrain failure

8 Shock and Vibration

Original data (GSB1) Fast Fourier transform (GSB1)

(a)(a)

(b)

(c)(c)

(b)

Original data (CMSA1) Fast Fourier transform (CMSA1)

(a)

(b)

(c) (c)

(b)

(a)

minus1minus05

005

1

Am

plitu

de (m

V)

01 09 10 05 06 07 080302 04Time (s)

01 02 03 04 05 06 07 08 09 10Time (s)

minus1minus05

005

1

Am

plitu

de (m

V)

minus20minus10

01020

Am

plitu

de (m

V)

01 09 10 05 06 07 080302 04Time (s)

minus20

minus10

0

10

20

Am

plitu

de (m

V)

01 02 03 04 05 06 07 08 09 10Time (s)

01 02 03 04 05 06 07 08 09 10Time (s)

minus20

minus10

0

10

20

Am

plitu

de (m

V)

01 02 03 04 05 06 07 08 09 10Time (s)

minus5

0

5

Am

plitu

de (m

V)

500 1000 15000Frequency (Hz)

00002000400060008

001

Am

plitu

de

500 1000 15000Frequency (Hz)

0

005

01

015

02

Am

plitu

de

500 1000 15000Frequency (Hz)

0

01

02

03

04

Am

plitu

de

500 1000 15000Frequency (Hz)

0001002003004

Am

plitu

de

500 1000 15000Frequency (Hz)

00005

0010015

002

Am

plitu

de

500 1000 15000Frequency (Hz)

00002000400060008

001

Am

plitu

de

Figure 6 MS signal during cement and glass damage in different loading stages (a) early stage (b) middle stage (c) poststage

Shock and Vibration 9

CSA2CSB2CSC2CMSA2GSB1GSB3

CSB3CSB1

CMSA1CMSA3GSB2

Loading stage

100

80

60

40

20

0

MS

even

t acc

umul

atio

n (

)

I II III IV V VI VII VIII IX X

Figure 7 The event accumulation variation during the loadingstage

and the rule of crack or cleat initiation and propagation inthe samples Furthermore the intrinsic resonant frequenciesof variations of materials also contribute to the monitoringresult

52MS Event Count and Event Rate Asmentioned above weused the STALTA method to recognize effective MS eventcounting the ring and event number During the detection ofeffective MS signal if the ratio of STA and LTA is over thethreshold the number counter of MS event and ring will betriggered but the difference is that a single event will not stopuntil the value goes back to being under the threshold whilethe ring number counts all single section over the criticalamplitude It can be seen from the study that the MS eventand ring count are quite different for these three kinds ofmaterials and this shows irregular characteristics even inthe same kind of material (Tables 2 and 3) An interestingphenomenon is that cement specimen procedures least MSevent in comparison with coal and glass However as theloading time for each sample is quite different the eventrate value is quite similar for all the samples The averageevent rate per second is around 2 but for cement sample likeCMSA1 and CMSA3 it is only 025 and 022

Figure 7 shows the total event accumulation ratio of thewhole loading process The variations tendencies are quitedifferent for different sample for one group of the samples(CSB2 CSA2 CSB3 GSB1 and GSB3) shows linear type ofincrease in the loading process while for the other seriesof samples (CMSA1 GSB2 CMSA2 and CMSA3) eventaccumulation increases smoothly at first (stages IndashV) andgrows sharply after that (stages VIndashX) These two types oftendencies give an illustration of MS activities in the whole

Specimen number

dc1dc2dc3dc4

dc5dc6dc7dc8

CSA

2

CMSA

2

GSB

1

GSB

3

GSB

2

CSB3

CSB1

CMSA

1

CMSA

3

00

02

04

06

08

10

Ener

gy p

ropo

rtio

n (

)

Figure 8 Energy proportion of WPT best tree decomposition

process and this phenomenon has great agreement with thestress and material damage rules

53The Energy Distribution Characteristics of MS Signal Theanalysis on energy distribution ofMS signal is based on resultofWPT best tree transformation It can be seen from Figure 1that the signal is decomposed into 8 decompositions denotedas dc1 to dc8 According to the node number the corre-sponding frequency bands of each node component are 0ndash469Hz 469ndash938Hz 938ndash1406Hz 1406ndash1875Hz 1875ndash3750Hz 3750ndash750Hz 750ndash10781Hz and 10781ndash1500Hzrespectively Examples of several specimens in terms ofenergy distribution of the eight decompositions are shownin Figure 8 Hereby the energy distribution analysis alsoshows the frequency-spectrum characteristics of differentmaterials It is obvious to see and conclude that all specimensmade of different materials are abundant with low frequencycomponents especially for dc1 However some differencesbetween each material type could also be found In the casesof glass specimens the total signal energy for each specimenin 0ndash469Hz is all more than 50 (629 958 and 507)As glass is a kind of typical brittle material there are alwaysbig fractures generated directly when it is compressed anddeformed This kind of rapid release of energy will limitthe energy accumulation and the big fractures all limit thespreading of stress wave Consequently the signal energyis mainly from low frequency domain By contrast theenergy proportions for cement produced signals are widelydistributed in all frequency domains mainly covering the 0ndash938Hz and 1406ndash750Hz But for coal the situation is muchmore complicated The main energy distribution domain forevery sample seems to change greatly in their medium and

10 Shock and Vibration

Table 2 Ring count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X TotalCSA2 6367 7147 8068 5044 4966 4792 3178 4998 2793 11400 58753CSB1 194 1666 892 140 59 22 71 174 2399 4145 9762CSB2 1797 1938 1430 1264 1746 1117 814 1363 932 1570 13971CSB3 13174 6847 8510 8543 6849 7595 8019 10858 7705 9417 87517CSC2 1596 6451 3153 569 640 123 0 122 177 3259 16090CMSA1 630 57 43 0 905 567 238 686 707 1686 5519CMSA2 243 0 152 25 482 211 362 597 1146 2350 5568CMSA3 1652 325 199 175 667 1203 1563 3438 11684 11132 32038GSB1 6961 5387 7107 4334 3437 4303 3160 2680 3460 1507 42336GSB2 2016 2521 2578 2613 3802 6703 17618 23977 23964 21802 107594GSB3 11958 9097 10882 16121 11292 9896 10314 11962 18636 17162 127320

Table 3 MS event count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X Total Event rateCSA2 311 294 302 219 239 226 159 238 121 368 2477 236CSB1 23 57 27 4 2 1 3 6 84 118 325 217CSB2 94 111 93 78 101 71 61 81 67 77 834 309CSB3 559 409 428 472 359 405 413 519 388 478 4430 242CSC2 83 132 84 14 11 3 0 2 4 87 420 14CMSA1 10 0 6 2 4 6 17 21 36 81 183 022CMSA2 28 3 2 0 37 21 10 36 36 73 246 025CMSA3 66 34 20 21 27 67 66 108 374 333 1116 13GSB1 220 162 187 133 76 117 103 91 96 58 1243 143GSB2 100 92 107 90 176 268 531 609 602 603 3178 195GSB3 401 323 382 505 378 335 342 417 540 535 4158 258

high frequency components Specifically CSA2 has morefrequency components for d8 and CSB1 has more for d5while CSB3 is abundant with d7 This may be mainly becauseof the differences for loading rate and coal powder sizes andof course the boundaries and interfaces inside the samples

54 Relationship between MS Signal and Loading Rate Toanalyze the microseismic characteristics of different materi-als different loading rate is set in the process of experimentAs mentioned above coal cement paste and glass speci-mens have quite different mechanic strength and materialcharacteristics Thus we set different loading rate variationfor each type of materials for coal it is 5ndash20Ns and forcement and glass it is 150ndash300Ns Taking coal samples asan example the statistics on CSB1 CSB2 and CSB3 showthat the loading peak force values increase as the loading rateset larger and the bigger the loading rate the more the MSevents and ring number activities recodedThe figure for thisis 9762 and 13971 to 87517 in ring number and 325834 to 4430in MS single event Furthermore similar result for cementand glass sample can be found regarding the variation ofloading rate We believe that this is because MS phenomenonis actually kind of stress wave and release of energy and largerloading speed results in more rapid energy accumulating and

releasing outThus there aremoreMS events and ring countswere founded during the whole loading process

55 Relationship between MS Signal and Coal Powder ParticleSize A group of coal specimens (CSA2 CSB2 and CSC2)is selected to analyze the effect of coal powder size on MScharacteristics As shown in Table 1 under the same loadingrate (10Ns) the peak values are 774 kN 272 kN and 318 kNfor specimens made by particles size in 0ndash025mm 025ndash05mm and 05ndash2mm Theoretically the smaller the size ofpowders used is the larger the strength of pressured briquettesamples should be for small particles could bond wellAlthough CSB2 which was made up of 025ndash05mm particlesize powders only got 272 kN loading peak value before theprimary damage the general trend between CSA2 and CSC2showed an obvious decrease of sample strength with powdersize going up As bigger strength means more loading time itis not surprising thatmoreMS events were recorded for CSA2and CSC2 (Table 2) On the contrary CSB2 has the largestevent rate and it is 309 events every second as shown inTable 3 Another feature by which powder size will influencethe man-made specimen structure is the homogeneity Thisis because specimens made by larger particle size of powderhave more cracks inside As a result it will take longer time

Shock and Vibration 11

during the crack closure stage and finally get into linear elasticstage Also as shown in Figure 7 the curves of MS eventaccumulation with loading stage for CSA2 and CSB2 increasemore linearly while for CSC2 made by largest powder itis quite different In summary although the loading timespecimen strength and events statistic for CSB2 show someabnormality comparedwith general trend from small powdersize to large powder size made specimen the global eventaccumulation has great consistency with theoretical analysis

6 Conclusion

A total of 11 experiment tests on coal cement paste andglass specimens were carried out under uniaxial compressioncondition Coal powder particle size material type andloading rate are selected to compare and analyzeMSAE char-acteristic In the meantime the MS event rate accumulatedringing number relationship between loading stress and MSevent energy distribution and frequency-time characteris-tics of MS signal produced during the damage of differentspecimens were also discussed in the work

To separate the MS signal from the noises pollutedsignals the combination of wavelet packet transform (WPT)and local mean decomposition (LMD)methodwas chosen todenoise and analyze all the signals collected The applicationof this new method proved that the WPT-LMD method hada good performance in denoising and filtering the MS dataand it also could be used as common tool in the issues offrequency-spectrum characteristics analysis of MS signal orMS-like nonstationary and nonlinear signal

The investigation result on theMS signal features of threecategories of materials showed the following

(1) In terms of the damage and deformation of coal andcement in the early stage few effective signals areproduced then specimens will experience a relativeclam middle stage and in the poststage where mainrupture occurred a large number of MS events wererecorded But for glass samples theMS event genera-tions were throughout the whole loading process Weconclude that this is because of the brittle and crispyfeatures of glass material which means less energystored and quickly released

(2) Also there is good corresponding relation betweenloading time and MS events and ring numbers thelonger loading time is the more MS events and ringnumbers are detected while for loading rate there isno obvious effect on MS

(3) In the case of relationship for coal powder par-ticle size and MS feature we found that the MSaccumulation patterns of small particles (0ndash05mm)made specimens be more linear while large particles(05ndash2mm) made specimens own typical nonlinearcharacteristics The latter will spend much more timeto close the crack and be crushed into small powdersThere seems to be a decrease trend of specimenstrengths with the powder size used from small tolarge but there is abnormal case of CSB2 Similar

conclusion could be made for event and ring numbercounts and event rate this may be caused by a failureof specimen preparation and this is supposed to beexamined in the further study

(4) From the frequency-spectrum point of view all testson differentmaterials showedwide frequency domaincharacteristics which are fallen in the domain of0ndash200Hz 300ndash500Hz and 1000ndash1500Hz Howeverit should be noted that the frequency domain ofglass specimens is mainly concentrated in the lowfrequency (0ndash938Hz) due to their extremely brittlemechanical characteristics

To sum up we believe that the outcome of the paperwill be of great significance to improve the dynamic disastersforecast and improve coal mining safety This may embodysome promotions in data processing by using theWPT-LMDmethod and provide a theoretical basis for understandingthe mechanism of MS activities due to the experimentalphenomenon and conclusions presented

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This study was supported by National Natural Science Foun-dation of China (Grant no 51274206) The correspondingauthor also acknowledged the financial support provided byChinese Scholarship Council (CSC)

References

[1] C U Grosse andM Ohtsu Acoustic Emission Testing SpringerBerlin Germany 2008

[2] H RHardy JrAcoustic EmissionMicroseismic Activity Volume1 Principles Techniques and Geotechnical Applications CRCPress 2005

[3] C-P Lu L-M Dou B Liu Y-S Xie and H-S Liu ldquoMicroseis-mic low-frequency precursor effect of bursting failure of coaland rockrdquo Journal of Applied Geophysics vol 79 pp 55ndash63 2012

[4] C-P Lu L-M Dou H Liu H-S Liu B Liu and B-B DuldquoCase study on microseismic effect of coal and gas outburstprocessrdquo International Journal of Rock Mechanics and MiningSciences vol 53 pp 101ndash110 2012

[5] S I Qin J Cheng and S Zhu ldquoThe application and prospect ofmicroseismic technique in coalminerdquo Procedia EnvironmentalSciences vol 12 pp 218ndash224 2012

[6] X H Liu C X Liu S Zhuang L Wang and S S LinldquoLaboratory studies on acoustic emission characteristics to coaldynamic response under variable accelerative loadrdquo AppliedMechanics and Materials vol 580ndash583 pp 623ndash627 2014

[7] N Cook ldquoThe seismic location of rockburstsrdquo in Proceedings ofthe 5th Rock Mechanics Symposium pp 463ndash516 1963

[8] A G Beattie ldquoAcoustic emission principles and instrumenta-tionrdquo Journal of Acoustic Emission vol 2 no 12 pp 95ndash1281983

12 Shock and Vibration

[9] L Obert ldquoThe microseismic method discovery and earlyhistoryrdquo in Proceedings of the 1st Conference on Acoustic Emis-sionMicroseismic Activity in Geologic Structures and Materialspp 11ndash12 1977

[10] I Yamada K Masuda and H Mizutani ldquoElectromagnetic andacoustic emission associated with rock fracturerdquo Physics of theEarth and Planetary Interiors vol 57 no 1-2 pp 157ndash168 1989

[11] C-P Lu L-M Dou N Zhang et al ldquoMicroseismic frequency-spectrum evolutionary rule of rockburst triggered by roof fallrdquoInternational Journal of Rock Mechanics and Mining Sciencesvol 64 pp 6ndash16 2013

[12] S Sasaki ldquoCharacteristics of microseismic events inducedduring hydraulic fracturing experiments at the Hijiori hot dryrock geothermal energy site Yamagata Japanrdquo Tectonophysicsvol 289 no 1ndash3 pp 171ndash188 1998

[13] F Spaepen ldquoAmicroscopicmechanism for steady state inhomo-geneous flow in metallic glassesrdquo Acta Metallurgica vol 25 no4 pp 407ndash415 1977

[14] P E Tikkanen ldquoNonlinear wavelet and wavelet packet denois-ing of electrocardiogram signalrdquo Biological Cybernetics vol 80no 4 pp 259ndash267 1999

[15] O A M Aly A S Omar and A Z Elsherbeni ldquoDetectionand localization of RF radar pulses in noise environments usingwavelet packet transform and higher order statisticsrdquo Progressin Electromagnetics Research vol 58 pp 301ndash317 2006

[16] A Kyprianou P L Lewin V Efthimiou A Stavrou and GE Georghiou ldquoWavelet packet denoising for online partialdischarge detection in cables and its application to experimentalfield resultsrdquoMeasurement Science and Technology vol 17 no 9pp 2367ndash2379 2006

[17] J S Smith ldquoThe local mean decomposition and its applicationto EEG perception datardquo Journal of the Royal Society Interfacevol 2 no 5 pp 443ndash454 2005

[18] Z Sun and C C Chang ldquoStructural damage assessment basedon wavelet packet transformrdquo Journal of Structural Engineeringvol 128 no 10 pp 1354ndash1361 2002

[19] J-D Wu and C-H Liu ldquoAn expert system for fault diagnosisin internal combustion engines using wavelet packet transformand neural networkrdquo Expert Systems with Applications vol 36no 3 pp 4278ndash4286 2009

[20] Y Wang Z He and Y Zi ldquoA demodulation method based onimproved local mean decomposition and its application in rub-impact fault diagnosisrdquo Measurement Science and Technologyvol 20 no 2 Article ID 025704 2009

[21] Y Wang Z He and Y Zi ldquoA comparative study on thelocal mean decomposition and empirical mode decompositionand their applications to rotating machinery health diagnosisrdquoJournal of Vibration and Acoustics vol 132 no 2 Article ID021010 2010

[22] A Trnkoczy ldquoUnderstanding and parameter setting ofSTALTA trigger algorithmrdquo in IASPEI New Manual ofSeismological Observatory Practice vol 2 pp 1ndash19 DeutschesGeoForschungsZentrum GFZ Potsdam Germany 2002

[23] V Vishal P G Ranjith and T N Singh ldquoAn experimentalinvestigation on behaviour of coal under fluid saturationusing acoustic emissionrdquo Journal of Natural Gas Science andEngineering vol 22 pp 428ndash436 2015

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Shock and Vibration 5

Table 1 Experimental parameters of specimens and work condition details

Specimen number Loading rate (Ns) Peak value (kN) Loading time (s) Special noteCSA2 10 774 1050 Particle size lt025mmCSB1 5 224 270 Particle size 025ndash050mmCSB2 10 272 150 Particle size 025ndash050mmCSB3 20 107 1830 Particle size 025ndash050mmCSC2 10 318 300 Particle size 050ndash200mmCMSA1 150 12322 910 Cement type PO 425CMSA2 200 16296 860 Cement type PO 425CMSA3 250 19659 820 Cement type PO 425GSB1 200 2963 1610 Layer count 10GSB2 250 20448 870 Layer count 10GSB3 300 4793 1630 Layer count 10

(2) Turn on the ZDKT-1 type dynamic disaster monitorapparatus start up the monitoring operation systemand set related initial parameters well for examplethe loading rate sample frequency and initial loadingforce

(3) Before recording the data verify that signal acquisi-tion module worked well and begin to record data

(4) Start the loading process and keep loading until thespecimens were completely damaged

(5) Stop loading and recording save the data file(6) Repeat the procedure again and run test on other

specimens

4 Experimental Result and Analysis

A series of uniaxial compression loading were conducted oncoal cement and glass specimensThe detailed experimentalparameters such as the loading rate loading last time andthe peak value of loading force are shown in Table 1 Dueto the space limitation only partial results of the tests foreach kind of specimen are presented in this paper To bemore specific they are actual 5 tests of coal specimens 3 testsof cement and 3 tests of glass But that is still enough toshow the characteristics changes of MS signal under variousloading conditions For instance CSB1 CSB2 and CSB3 areused for discussion on MS feature changes with the variationof the loading rate CSA2 CSB2 and CSC2 are selectedto give a comparison between samples made by differentparticle size of coal powders and of course MS data recordedfrom different materials are mainly used to compare thecharacteristics of MS events from each other

The material failure process is accompanied by MSAEwhich is actually a form of quick release of stored energySpecimens here were investigated for their mechanical per-formances with microseismicity patterns throughout thewhole loading process Theoretically speaking the continu-ous loading on material will cause the accumulation of strainenergy inside once the accumulated energy exceeds the ulti-mate value limited by the mechanical properties of materialsinternal fracture generation and external damage will occur

Generally there are five detailed stages of the damage anddeformation of materials under uniaxial compression thatis crack closure crack initiation secondary cracking crackcoalescence and crack damage [23] However the actualstress loading patterns will be affected a lot due to themechanical properties of materials The MS activities andloading stress variations with the loading time increase areillustrated in Figure 4 To make it easier to be analyzed eachtest was divided into 10 loading sections according to totalloading timeWe defined sections IndashIII as early stage sectionsIVndashVII as middle stage and sections VIIIndashX as poststage

In case of coal specimens MS activities occurred at bothearly stage and poststage but the primary fracture period ofcoal occurred in the poststage As illustrated in Figure 4 atearly loading stage of coal samples there were several MSactivities produced after that before the peak of loadingstress only few MS activities could be observed most ofthe MS events were collected after the loading peak valueThese briquette coal specimens mostly were destroyed understress less than 10KN and produce MS signals with lowestamplitude for 15mV in comparison with cement and glassspecimensThe patterns of MS events for cement samples arevery similar to coal most activities are collected in primaryfracture and early stage but it should be noticed in themiddleof loading just before the main fracture happens that theMS release is also very active With respect to event rate thevalues for cement samples are lowest in all three materialsFor the glass samples due to their larger uniaxial strengthand layer distribution more MS activities are found throughthewhole loading process Loading on glass samples obtainedthe largest loading stress and the highest MS amplituderanging from 250 kN to 300 kN and approximately 100mVrespectively the same value to cement specimen is rangingfrom 150 kN to 200 kN and 60mV

The LMD result of CSC2 is shown in Figure 5 Thedenoised signal which is obtained by filtering the originalsignal is decomposed into several PFs These PFs havedifferent characteristics in both frequency and time domainThe frequency distributions are separated into three seriesThe high frequency is all collected at the early stage andmainfracture stage with highest amplitude and the frequency isabout 1000ndash1200Hz The medium frequency is the second

6 Shock and Vibration

III

III

IV

V

VI

VIIVIII

IXX

CSA2MSF

CMSA1MSF

GSB1MSF

III

IIIIV

VVI

VIIVIII

IXX

III

IIIIV

VVI

VIIVIII

IXX

III

IIIIV

V

VIVII

VIIIIX

X

I IIIII

IVV

VI

VIIVIII

IXX

III

IIIIV

V VI

VIIVIII

IXX

minus15

minus10

minus5

0

5

10

15M

S (m

V)

200 400 600 800 1000 12000Time (s)

0

2

4

6

8

F (k

N)

minus20

minus15

minus10

minus5

0

5

10

15

20

MS

(mV

)

50 100 150 200 250 300 3500Time (s)

0

1

2

3

4

F (k

N)

CSC2MSF

CMSA2MSF

GSB2MSF

minus60

minus40

minus20

0

20

40

60

MS

(mV

)

150 300 450 600 750 9000Time (s)

0

50

100

150

200

F (k

N)

0

50

100

150

F (k

N)

200 400 600 800 10000Time (s)

minus60

minus40

minus20

0

20

40

60

MS

(mV

)

minus100

minus50

0

50

100

MS

(mV

)

200 400 600 800 1000 1200 1400 16000Time (s)

0

100

200

300

F (k

N)

150 300 450 600 750 9000Time (s)

0

50

100

150

200

250

F (k

N)

minus100

minus50

0

50

100

MS

(mV

)

Figure 4 The MS events signal with increase of loading stress and time

most abundant withMS events its frequency ranges from 100to 500Hz Low frequency decomposition owns low ampli-tude but it should not be overlooked for low frequency signal(lower than 100Hz) usually spread further in real practice Asits illustration of thewhole loading process it seems that thereare not toomany events that could be found in the figureThedetailed characteristics will be shown in the following part

5 Discussion

51 Spectrum Characteristics in Different Loading Stages Asstated above every test is divided into three stages and tensections To analyze the characteristics of different materialsin different loading stages typical microseismic events fromeach stage are selected Figure 6 illustrates diagrammatically

Shock and Vibration 7

100 200 3000Time (s)

minus005

0

005

PF-5

(mV

)

minus02

0

02

PF-4

(mV

)

100 200 3000Time (s)

100 200 3000Time (s)

minus05

0

05

PF-3

(mV

)

100 200 3000Time (s)

minus5

0

5

PF-2

(mV

)

minus10

0

10

PF-1

(mV

)

100 200 3000Time (s)

100 200 3000Time (s)

minus10

0

10

MS

(mV

)times10

minus3

0

1

2

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus3

0

1

2

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus4

500 1000 15000Frequency (Hz)

0

2

4

Am

plitu

de

times10minus4

0

05

1

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus4

500 1000 15000Frequency (Hz)

0

05

1

Am

plitu

de

times10minus5

500 1000 15000Frequency (Hz)

0

5

Am

plitu

de

Figure 5 A typical LMD result of CSC2

sections of typicalMS events data of cement sample (CMSA1)and glass sample (GSB1) in early stage middle stage andpoststage In terms of cement specimen the maximumamplitude of signal increases obviously with the loadingstage the real amplitude from 1mV in early stage to 20mVin the poststage and relative amplitude of FFT from 001 to004 From the frequency domain point of view before thecoming of main fracture in poststage the frequency of signalis mainly from 0 to 500Hz with maximum frequency around100ndash500Hz while in the poststage the main frequency isfrom 1000 to 1500Hz Also it should be noticed that in

every stage theMS signals all owned relativelywide frequencydomain distribution Analysis result of the same method oncoal specimen is very similar to cement However for glasssamples the maximum amplitude is not in the poststageand the frequency distribution is also different As shown inFigure 4 the MS events are detected in the whole loadingprocess Theoretically speaking coal and cement sampleswere similarly made up of powders and couplant to someextent these two kinds of samples are homogeneous andisotropic while glass samples are combination of severallayers This yields the difference of inter- or intragrain failure

8 Shock and Vibration

Original data (GSB1) Fast Fourier transform (GSB1)

(a)(a)

(b)

(c)(c)

(b)

Original data (CMSA1) Fast Fourier transform (CMSA1)

(a)

(b)

(c) (c)

(b)

(a)

minus1minus05

005

1

Am

plitu

de (m

V)

01 09 10 05 06 07 080302 04Time (s)

01 02 03 04 05 06 07 08 09 10Time (s)

minus1minus05

005

1

Am

plitu

de (m

V)

minus20minus10

01020

Am

plitu

de (m

V)

01 09 10 05 06 07 080302 04Time (s)

minus20

minus10

0

10

20

Am

plitu

de (m

V)

01 02 03 04 05 06 07 08 09 10Time (s)

01 02 03 04 05 06 07 08 09 10Time (s)

minus20

minus10

0

10

20

Am

plitu

de (m

V)

01 02 03 04 05 06 07 08 09 10Time (s)

minus5

0

5

Am

plitu

de (m

V)

500 1000 15000Frequency (Hz)

00002000400060008

001

Am

plitu

de

500 1000 15000Frequency (Hz)

0

005

01

015

02

Am

plitu

de

500 1000 15000Frequency (Hz)

0

01

02

03

04

Am

plitu

de

500 1000 15000Frequency (Hz)

0001002003004

Am

plitu

de

500 1000 15000Frequency (Hz)

00005

0010015

002

Am

plitu

de

500 1000 15000Frequency (Hz)

00002000400060008

001

Am

plitu

de

Figure 6 MS signal during cement and glass damage in different loading stages (a) early stage (b) middle stage (c) poststage

Shock and Vibration 9

CSA2CSB2CSC2CMSA2GSB1GSB3

CSB3CSB1

CMSA1CMSA3GSB2

Loading stage

100

80

60

40

20

0

MS

even

t acc

umul

atio

n (

)

I II III IV V VI VII VIII IX X

Figure 7 The event accumulation variation during the loadingstage

and the rule of crack or cleat initiation and propagation inthe samples Furthermore the intrinsic resonant frequenciesof variations of materials also contribute to the monitoringresult

52MS Event Count and Event Rate Asmentioned above weused the STALTA method to recognize effective MS eventcounting the ring and event number During the detection ofeffective MS signal if the ratio of STA and LTA is over thethreshold the number counter of MS event and ring will betriggered but the difference is that a single event will not stopuntil the value goes back to being under the threshold whilethe ring number counts all single section over the criticalamplitude It can be seen from the study that the MS eventand ring count are quite different for these three kinds ofmaterials and this shows irregular characteristics even inthe same kind of material (Tables 2 and 3) An interestingphenomenon is that cement specimen procedures least MSevent in comparison with coal and glass However as theloading time for each sample is quite different the eventrate value is quite similar for all the samples The averageevent rate per second is around 2 but for cement sample likeCMSA1 and CMSA3 it is only 025 and 022

Figure 7 shows the total event accumulation ratio of thewhole loading process The variations tendencies are quitedifferent for different sample for one group of the samples(CSB2 CSA2 CSB3 GSB1 and GSB3) shows linear type ofincrease in the loading process while for the other seriesof samples (CMSA1 GSB2 CMSA2 and CMSA3) eventaccumulation increases smoothly at first (stages IndashV) andgrows sharply after that (stages VIndashX) These two types oftendencies give an illustration of MS activities in the whole

Specimen number

dc1dc2dc3dc4

dc5dc6dc7dc8

CSA

2

CMSA

2

GSB

1

GSB

3

GSB

2

CSB3

CSB1

CMSA

1

CMSA

3

00

02

04

06

08

10

Ener

gy p

ropo

rtio

n (

)

Figure 8 Energy proportion of WPT best tree decomposition

process and this phenomenon has great agreement with thestress and material damage rules

53The Energy Distribution Characteristics of MS Signal Theanalysis on energy distribution ofMS signal is based on resultofWPT best tree transformation It can be seen from Figure 1that the signal is decomposed into 8 decompositions denotedas dc1 to dc8 According to the node number the corre-sponding frequency bands of each node component are 0ndash469Hz 469ndash938Hz 938ndash1406Hz 1406ndash1875Hz 1875ndash3750Hz 3750ndash750Hz 750ndash10781Hz and 10781ndash1500Hzrespectively Examples of several specimens in terms ofenergy distribution of the eight decompositions are shownin Figure 8 Hereby the energy distribution analysis alsoshows the frequency-spectrum characteristics of differentmaterials It is obvious to see and conclude that all specimensmade of different materials are abundant with low frequencycomponents especially for dc1 However some differencesbetween each material type could also be found In the casesof glass specimens the total signal energy for each specimenin 0ndash469Hz is all more than 50 (629 958 and 507)As glass is a kind of typical brittle material there are alwaysbig fractures generated directly when it is compressed anddeformed This kind of rapid release of energy will limitthe energy accumulation and the big fractures all limit thespreading of stress wave Consequently the signal energyis mainly from low frequency domain By contrast theenergy proportions for cement produced signals are widelydistributed in all frequency domains mainly covering the 0ndash938Hz and 1406ndash750Hz But for coal the situation is muchmore complicated The main energy distribution domain forevery sample seems to change greatly in their medium and

10 Shock and Vibration

Table 2 Ring count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X TotalCSA2 6367 7147 8068 5044 4966 4792 3178 4998 2793 11400 58753CSB1 194 1666 892 140 59 22 71 174 2399 4145 9762CSB2 1797 1938 1430 1264 1746 1117 814 1363 932 1570 13971CSB3 13174 6847 8510 8543 6849 7595 8019 10858 7705 9417 87517CSC2 1596 6451 3153 569 640 123 0 122 177 3259 16090CMSA1 630 57 43 0 905 567 238 686 707 1686 5519CMSA2 243 0 152 25 482 211 362 597 1146 2350 5568CMSA3 1652 325 199 175 667 1203 1563 3438 11684 11132 32038GSB1 6961 5387 7107 4334 3437 4303 3160 2680 3460 1507 42336GSB2 2016 2521 2578 2613 3802 6703 17618 23977 23964 21802 107594GSB3 11958 9097 10882 16121 11292 9896 10314 11962 18636 17162 127320

Table 3 MS event count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X Total Event rateCSA2 311 294 302 219 239 226 159 238 121 368 2477 236CSB1 23 57 27 4 2 1 3 6 84 118 325 217CSB2 94 111 93 78 101 71 61 81 67 77 834 309CSB3 559 409 428 472 359 405 413 519 388 478 4430 242CSC2 83 132 84 14 11 3 0 2 4 87 420 14CMSA1 10 0 6 2 4 6 17 21 36 81 183 022CMSA2 28 3 2 0 37 21 10 36 36 73 246 025CMSA3 66 34 20 21 27 67 66 108 374 333 1116 13GSB1 220 162 187 133 76 117 103 91 96 58 1243 143GSB2 100 92 107 90 176 268 531 609 602 603 3178 195GSB3 401 323 382 505 378 335 342 417 540 535 4158 258

high frequency components Specifically CSA2 has morefrequency components for d8 and CSB1 has more for d5while CSB3 is abundant with d7 This may be mainly becauseof the differences for loading rate and coal powder sizes andof course the boundaries and interfaces inside the samples

54 Relationship between MS Signal and Loading Rate Toanalyze the microseismic characteristics of different materi-als different loading rate is set in the process of experimentAs mentioned above coal cement paste and glass speci-mens have quite different mechanic strength and materialcharacteristics Thus we set different loading rate variationfor each type of materials for coal it is 5ndash20Ns and forcement and glass it is 150ndash300Ns Taking coal samples asan example the statistics on CSB1 CSB2 and CSB3 showthat the loading peak force values increase as the loading rateset larger and the bigger the loading rate the more the MSevents and ring number activities recodedThe figure for thisis 9762 and 13971 to 87517 in ring number and 325834 to 4430in MS single event Furthermore similar result for cementand glass sample can be found regarding the variation ofloading rate We believe that this is because MS phenomenonis actually kind of stress wave and release of energy and largerloading speed results in more rapid energy accumulating and

releasing outThus there aremoreMS events and ring countswere founded during the whole loading process

55 Relationship between MS Signal and Coal Powder ParticleSize A group of coal specimens (CSA2 CSB2 and CSC2)is selected to analyze the effect of coal powder size on MScharacteristics As shown in Table 1 under the same loadingrate (10Ns) the peak values are 774 kN 272 kN and 318 kNfor specimens made by particles size in 0ndash025mm 025ndash05mm and 05ndash2mm Theoretically the smaller the size ofpowders used is the larger the strength of pressured briquettesamples should be for small particles could bond wellAlthough CSB2 which was made up of 025ndash05mm particlesize powders only got 272 kN loading peak value before theprimary damage the general trend between CSA2 and CSC2showed an obvious decrease of sample strength with powdersize going up As bigger strength means more loading time itis not surprising thatmoreMS events were recorded for CSA2and CSC2 (Table 2) On the contrary CSB2 has the largestevent rate and it is 309 events every second as shown inTable 3 Another feature by which powder size will influencethe man-made specimen structure is the homogeneity Thisis because specimens made by larger particle size of powderhave more cracks inside As a result it will take longer time

Shock and Vibration 11

during the crack closure stage and finally get into linear elasticstage Also as shown in Figure 7 the curves of MS eventaccumulation with loading stage for CSA2 and CSB2 increasemore linearly while for CSC2 made by largest powder itis quite different In summary although the loading timespecimen strength and events statistic for CSB2 show someabnormality comparedwith general trend from small powdersize to large powder size made specimen the global eventaccumulation has great consistency with theoretical analysis

6 Conclusion

A total of 11 experiment tests on coal cement paste andglass specimens were carried out under uniaxial compressioncondition Coal powder particle size material type andloading rate are selected to compare and analyzeMSAE char-acteristic In the meantime the MS event rate accumulatedringing number relationship between loading stress and MSevent energy distribution and frequency-time characteris-tics of MS signal produced during the damage of differentspecimens were also discussed in the work

To separate the MS signal from the noises pollutedsignals the combination of wavelet packet transform (WPT)and local mean decomposition (LMD)methodwas chosen todenoise and analyze all the signals collected The applicationof this new method proved that the WPT-LMD method hada good performance in denoising and filtering the MS dataand it also could be used as common tool in the issues offrequency-spectrum characteristics analysis of MS signal orMS-like nonstationary and nonlinear signal

The investigation result on theMS signal features of threecategories of materials showed the following

(1) In terms of the damage and deformation of coal andcement in the early stage few effective signals areproduced then specimens will experience a relativeclam middle stage and in the poststage where mainrupture occurred a large number of MS events wererecorded But for glass samples theMS event genera-tions were throughout the whole loading process Weconclude that this is because of the brittle and crispyfeatures of glass material which means less energystored and quickly released

(2) Also there is good corresponding relation betweenloading time and MS events and ring numbers thelonger loading time is the more MS events and ringnumbers are detected while for loading rate there isno obvious effect on MS

(3) In the case of relationship for coal powder par-ticle size and MS feature we found that the MSaccumulation patterns of small particles (0ndash05mm)made specimens be more linear while large particles(05ndash2mm) made specimens own typical nonlinearcharacteristics The latter will spend much more timeto close the crack and be crushed into small powdersThere seems to be a decrease trend of specimenstrengths with the powder size used from small tolarge but there is abnormal case of CSB2 Similar

conclusion could be made for event and ring numbercounts and event rate this may be caused by a failureof specimen preparation and this is supposed to beexamined in the further study

(4) From the frequency-spectrum point of view all testson differentmaterials showedwide frequency domaincharacteristics which are fallen in the domain of0ndash200Hz 300ndash500Hz and 1000ndash1500Hz Howeverit should be noted that the frequency domain ofglass specimens is mainly concentrated in the lowfrequency (0ndash938Hz) due to their extremely brittlemechanical characteristics

To sum up we believe that the outcome of the paperwill be of great significance to improve the dynamic disastersforecast and improve coal mining safety This may embodysome promotions in data processing by using theWPT-LMDmethod and provide a theoretical basis for understandingthe mechanism of MS activities due to the experimentalphenomenon and conclusions presented

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This study was supported by National Natural Science Foun-dation of China (Grant no 51274206) The correspondingauthor also acknowledged the financial support provided byChinese Scholarship Council (CSC)

References

[1] C U Grosse andM Ohtsu Acoustic Emission Testing SpringerBerlin Germany 2008

[2] H RHardy JrAcoustic EmissionMicroseismic Activity Volume1 Principles Techniques and Geotechnical Applications CRCPress 2005

[3] C-P Lu L-M Dou B Liu Y-S Xie and H-S Liu ldquoMicroseis-mic low-frequency precursor effect of bursting failure of coaland rockrdquo Journal of Applied Geophysics vol 79 pp 55ndash63 2012

[4] C-P Lu L-M Dou H Liu H-S Liu B Liu and B-B DuldquoCase study on microseismic effect of coal and gas outburstprocessrdquo International Journal of Rock Mechanics and MiningSciences vol 53 pp 101ndash110 2012

[5] S I Qin J Cheng and S Zhu ldquoThe application and prospect ofmicroseismic technique in coalminerdquo Procedia EnvironmentalSciences vol 12 pp 218ndash224 2012

[6] X H Liu C X Liu S Zhuang L Wang and S S LinldquoLaboratory studies on acoustic emission characteristics to coaldynamic response under variable accelerative loadrdquo AppliedMechanics and Materials vol 580ndash583 pp 623ndash627 2014

[7] N Cook ldquoThe seismic location of rockburstsrdquo in Proceedings ofthe 5th Rock Mechanics Symposium pp 463ndash516 1963

[8] A G Beattie ldquoAcoustic emission principles and instrumenta-tionrdquo Journal of Acoustic Emission vol 2 no 12 pp 95ndash1281983

12 Shock and Vibration

[9] L Obert ldquoThe microseismic method discovery and earlyhistoryrdquo in Proceedings of the 1st Conference on Acoustic Emis-sionMicroseismic Activity in Geologic Structures and Materialspp 11ndash12 1977

[10] I Yamada K Masuda and H Mizutani ldquoElectromagnetic andacoustic emission associated with rock fracturerdquo Physics of theEarth and Planetary Interiors vol 57 no 1-2 pp 157ndash168 1989

[11] C-P Lu L-M Dou N Zhang et al ldquoMicroseismic frequency-spectrum evolutionary rule of rockburst triggered by roof fallrdquoInternational Journal of Rock Mechanics and Mining Sciencesvol 64 pp 6ndash16 2013

[12] S Sasaki ldquoCharacteristics of microseismic events inducedduring hydraulic fracturing experiments at the Hijiori hot dryrock geothermal energy site Yamagata Japanrdquo Tectonophysicsvol 289 no 1ndash3 pp 171ndash188 1998

[13] F Spaepen ldquoAmicroscopicmechanism for steady state inhomo-geneous flow in metallic glassesrdquo Acta Metallurgica vol 25 no4 pp 407ndash415 1977

[14] P E Tikkanen ldquoNonlinear wavelet and wavelet packet denois-ing of electrocardiogram signalrdquo Biological Cybernetics vol 80no 4 pp 259ndash267 1999

[15] O A M Aly A S Omar and A Z Elsherbeni ldquoDetectionand localization of RF radar pulses in noise environments usingwavelet packet transform and higher order statisticsrdquo Progressin Electromagnetics Research vol 58 pp 301ndash317 2006

[16] A Kyprianou P L Lewin V Efthimiou A Stavrou and GE Georghiou ldquoWavelet packet denoising for online partialdischarge detection in cables and its application to experimentalfield resultsrdquoMeasurement Science and Technology vol 17 no 9pp 2367ndash2379 2006

[17] J S Smith ldquoThe local mean decomposition and its applicationto EEG perception datardquo Journal of the Royal Society Interfacevol 2 no 5 pp 443ndash454 2005

[18] Z Sun and C C Chang ldquoStructural damage assessment basedon wavelet packet transformrdquo Journal of Structural Engineeringvol 128 no 10 pp 1354ndash1361 2002

[19] J-D Wu and C-H Liu ldquoAn expert system for fault diagnosisin internal combustion engines using wavelet packet transformand neural networkrdquo Expert Systems with Applications vol 36no 3 pp 4278ndash4286 2009

[20] Y Wang Z He and Y Zi ldquoA demodulation method based onimproved local mean decomposition and its application in rub-impact fault diagnosisrdquo Measurement Science and Technologyvol 20 no 2 Article ID 025704 2009

[21] Y Wang Z He and Y Zi ldquoA comparative study on thelocal mean decomposition and empirical mode decompositionand their applications to rotating machinery health diagnosisrdquoJournal of Vibration and Acoustics vol 132 no 2 Article ID021010 2010

[22] A Trnkoczy ldquoUnderstanding and parameter setting ofSTALTA trigger algorithmrdquo in IASPEI New Manual ofSeismological Observatory Practice vol 2 pp 1ndash19 DeutschesGeoForschungsZentrum GFZ Potsdam Germany 2002

[23] V Vishal P G Ranjith and T N Singh ldquoAn experimentalinvestigation on behaviour of coal under fluid saturationusing acoustic emissionrdquo Journal of Natural Gas Science andEngineering vol 22 pp 428ndash436 2015

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International Journal of

6 Shock and Vibration

III

III

IV

V

VI

VIIVIII

IXX

CSA2MSF

CMSA1MSF

GSB1MSF

III

IIIIV

VVI

VIIVIII

IXX

III

IIIIV

VVI

VIIVIII

IXX

III

IIIIV

V

VIVII

VIIIIX

X

I IIIII

IVV

VI

VIIVIII

IXX

III

IIIIV

V VI

VIIVIII

IXX

minus15

minus10

minus5

0

5

10

15M

S (m

V)

200 400 600 800 1000 12000Time (s)

0

2

4

6

8

F (k

N)

minus20

minus15

minus10

minus5

0

5

10

15

20

MS

(mV

)

50 100 150 200 250 300 3500Time (s)

0

1

2

3

4

F (k

N)

CSC2MSF

CMSA2MSF

GSB2MSF

minus60

minus40

minus20

0

20

40

60

MS

(mV

)

150 300 450 600 750 9000Time (s)

0

50

100

150

200

F (k

N)

0

50

100

150

F (k

N)

200 400 600 800 10000Time (s)

minus60

minus40

minus20

0

20

40

60

MS

(mV

)

minus100

minus50

0

50

100

MS

(mV

)

200 400 600 800 1000 1200 1400 16000Time (s)

0

100

200

300

F (k

N)

150 300 450 600 750 9000Time (s)

0

50

100

150

200

250

F (k

N)

minus100

minus50

0

50

100

MS

(mV

)

Figure 4 The MS events signal with increase of loading stress and time

most abundant withMS events its frequency ranges from 100to 500Hz Low frequency decomposition owns low ampli-tude but it should not be overlooked for low frequency signal(lower than 100Hz) usually spread further in real practice Asits illustration of thewhole loading process it seems that thereare not toomany events that could be found in the figureThedetailed characteristics will be shown in the following part

5 Discussion

51 Spectrum Characteristics in Different Loading Stages Asstated above every test is divided into three stages and tensections To analyze the characteristics of different materialsin different loading stages typical microseismic events fromeach stage are selected Figure 6 illustrates diagrammatically

Shock and Vibration 7

100 200 3000Time (s)

minus005

0

005

PF-5

(mV

)

minus02

0

02

PF-4

(mV

)

100 200 3000Time (s)

100 200 3000Time (s)

minus05

0

05

PF-3

(mV

)

100 200 3000Time (s)

minus5

0

5

PF-2

(mV

)

minus10

0

10

PF-1

(mV

)

100 200 3000Time (s)

100 200 3000Time (s)

minus10

0

10

MS

(mV

)times10

minus3

0

1

2

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus3

0

1

2

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus4

500 1000 15000Frequency (Hz)

0

2

4

Am

plitu

de

times10minus4

0

05

1

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus4

500 1000 15000Frequency (Hz)

0

05

1

Am

plitu

de

times10minus5

500 1000 15000Frequency (Hz)

0

5

Am

plitu

de

Figure 5 A typical LMD result of CSC2

sections of typicalMS events data of cement sample (CMSA1)and glass sample (GSB1) in early stage middle stage andpoststage In terms of cement specimen the maximumamplitude of signal increases obviously with the loadingstage the real amplitude from 1mV in early stage to 20mVin the poststage and relative amplitude of FFT from 001 to004 From the frequency domain point of view before thecoming of main fracture in poststage the frequency of signalis mainly from 0 to 500Hz with maximum frequency around100ndash500Hz while in the poststage the main frequency isfrom 1000 to 1500Hz Also it should be noticed that in

every stage theMS signals all owned relativelywide frequencydomain distribution Analysis result of the same method oncoal specimen is very similar to cement However for glasssamples the maximum amplitude is not in the poststageand the frequency distribution is also different As shown inFigure 4 the MS events are detected in the whole loadingprocess Theoretically speaking coal and cement sampleswere similarly made up of powders and couplant to someextent these two kinds of samples are homogeneous andisotropic while glass samples are combination of severallayers This yields the difference of inter- or intragrain failure

8 Shock and Vibration

Original data (GSB1) Fast Fourier transform (GSB1)

(a)(a)

(b)

(c)(c)

(b)

Original data (CMSA1) Fast Fourier transform (CMSA1)

(a)

(b)

(c) (c)

(b)

(a)

minus1minus05

005

1

Am

plitu

de (m

V)

01 09 10 05 06 07 080302 04Time (s)

01 02 03 04 05 06 07 08 09 10Time (s)

minus1minus05

005

1

Am

plitu

de (m

V)

minus20minus10

01020

Am

plitu

de (m

V)

01 09 10 05 06 07 080302 04Time (s)

minus20

minus10

0

10

20

Am

plitu

de (m

V)

01 02 03 04 05 06 07 08 09 10Time (s)

01 02 03 04 05 06 07 08 09 10Time (s)

minus20

minus10

0

10

20

Am

plitu

de (m

V)

01 02 03 04 05 06 07 08 09 10Time (s)

minus5

0

5

Am

plitu

de (m

V)

500 1000 15000Frequency (Hz)

00002000400060008

001

Am

plitu

de

500 1000 15000Frequency (Hz)

0

005

01

015

02

Am

plitu

de

500 1000 15000Frequency (Hz)

0

01

02

03

04

Am

plitu

de

500 1000 15000Frequency (Hz)

0001002003004

Am

plitu

de

500 1000 15000Frequency (Hz)

00005

0010015

002

Am

plitu

de

500 1000 15000Frequency (Hz)

00002000400060008

001

Am

plitu

de

Figure 6 MS signal during cement and glass damage in different loading stages (a) early stage (b) middle stage (c) poststage

Shock and Vibration 9

CSA2CSB2CSC2CMSA2GSB1GSB3

CSB3CSB1

CMSA1CMSA3GSB2

Loading stage

100

80

60

40

20

0

MS

even

t acc

umul

atio

n (

)

I II III IV V VI VII VIII IX X

Figure 7 The event accumulation variation during the loadingstage

and the rule of crack or cleat initiation and propagation inthe samples Furthermore the intrinsic resonant frequenciesof variations of materials also contribute to the monitoringresult

52MS Event Count and Event Rate Asmentioned above weused the STALTA method to recognize effective MS eventcounting the ring and event number During the detection ofeffective MS signal if the ratio of STA and LTA is over thethreshold the number counter of MS event and ring will betriggered but the difference is that a single event will not stopuntil the value goes back to being under the threshold whilethe ring number counts all single section over the criticalamplitude It can be seen from the study that the MS eventand ring count are quite different for these three kinds ofmaterials and this shows irregular characteristics even inthe same kind of material (Tables 2 and 3) An interestingphenomenon is that cement specimen procedures least MSevent in comparison with coal and glass However as theloading time for each sample is quite different the eventrate value is quite similar for all the samples The averageevent rate per second is around 2 but for cement sample likeCMSA1 and CMSA3 it is only 025 and 022

Figure 7 shows the total event accumulation ratio of thewhole loading process The variations tendencies are quitedifferent for different sample for one group of the samples(CSB2 CSA2 CSB3 GSB1 and GSB3) shows linear type ofincrease in the loading process while for the other seriesof samples (CMSA1 GSB2 CMSA2 and CMSA3) eventaccumulation increases smoothly at first (stages IndashV) andgrows sharply after that (stages VIndashX) These two types oftendencies give an illustration of MS activities in the whole

Specimen number

dc1dc2dc3dc4

dc5dc6dc7dc8

CSA

2

CMSA

2

GSB

1

GSB

3

GSB

2

CSB3

CSB1

CMSA

1

CMSA

3

00

02

04

06

08

10

Ener

gy p

ropo

rtio

n (

)

Figure 8 Energy proportion of WPT best tree decomposition

process and this phenomenon has great agreement with thestress and material damage rules

53The Energy Distribution Characteristics of MS Signal Theanalysis on energy distribution ofMS signal is based on resultofWPT best tree transformation It can be seen from Figure 1that the signal is decomposed into 8 decompositions denotedas dc1 to dc8 According to the node number the corre-sponding frequency bands of each node component are 0ndash469Hz 469ndash938Hz 938ndash1406Hz 1406ndash1875Hz 1875ndash3750Hz 3750ndash750Hz 750ndash10781Hz and 10781ndash1500Hzrespectively Examples of several specimens in terms ofenergy distribution of the eight decompositions are shownin Figure 8 Hereby the energy distribution analysis alsoshows the frequency-spectrum characteristics of differentmaterials It is obvious to see and conclude that all specimensmade of different materials are abundant with low frequencycomponents especially for dc1 However some differencesbetween each material type could also be found In the casesof glass specimens the total signal energy for each specimenin 0ndash469Hz is all more than 50 (629 958 and 507)As glass is a kind of typical brittle material there are alwaysbig fractures generated directly when it is compressed anddeformed This kind of rapid release of energy will limitthe energy accumulation and the big fractures all limit thespreading of stress wave Consequently the signal energyis mainly from low frequency domain By contrast theenergy proportions for cement produced signals are widelydistributed in all frequency domains mainly covering the 0ndash938Hz and 1406ndash750Hz But for coal the situation is muchmore complicated The main energy distribution domain forevery sample seems to change greatly in their medium and

10 Shock and Vibration

Table 2 Ring count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X TotalCSA2 6367 7147 8068 5044 4966 4792 3178 4998 2793 11400 58753CSB1 194 1666 892 140 59 22 71 174 2399 4145 9762CSB2 1797 1938 1430 1264 1746 1117 814 1363 932 1570 13971CSB3 13174 6847 8510 8543 6849 7595 8019 10858 7705 9417 87517CSC2 1596 6451 3153 569 640 123 0 122 177 3259 16090CMSA1 630 57 43 0 905 567 238 686 707 1686 5519CMSA2 243 0 152 25 482 211 362 597 1146 2350 5568CMSA3 1652 325 199 175 667 1203 1563 3438 11684 11132 32038GSB1 6961 5387 7107 4334 3437 4303 3160 2680 3460 1507 42336GSB2 2016 2521 2578 2613 3802 6703 17618 23977 23964 21802 107594GSB3 11958 9097 10882 16121 11292 9896 10314 11962 18636 17162 127320

Table 3 MS event count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X Total Event rateCSA2 311 294 302 219 239 226 159 238 121 368 2477 236CSB1 23 57 27 4 2 1 3 6 84 118 325 217CSB2 94 111 93 78 101 71 61 81 67 77 834 309CSB3 559 409 428 472 359 405 413 519 388 478 4430 242CSC2 83 132 84 14 11 3 0 2 4 87 420 14CMSA1 10 0 6 2 4 6 17 21 36 81 183 022CMSA2 28 3 2 0 37 21 10 36 36 73 246 025CMSA3 66 34 20 21 27 67 66 108 374 333 1116 13GSB1 220 162 187 133 76 117 103 91 96 58 1243 143GSB2 100 92 107 90 176 268 531 609 602 603 3178 195GSB3 401 323 382 505 378 335 342 417 540 535 4158 258

high frequency components Specifically CSA2 has morefrequency components for d8 and CSB1 has more for d5while CSB3 is abundant with d7 This may be mainly becauseof the differences for loading rate and coal powder sizes andof course the boundaries and interfaces inside the samples

54 Relationship between MS Signal and Loading Rate Toanalyze the microseismic characteristics of different materi-als different loading rate is set in the process of experimentAs mentioned above coal cement paste and glass speci-mens have quite different mechanic strength and materialcharacteristics Thus we set different loading rate variationfor each type of materials for coal it is 5ndash20Ns and forcement and glass it is 150ndash300Ns Taking coal samples asan example the statistics on CSB1 CSB2 and CSB3 showthat the loading peak force values increase as the loading rateset larger and the bigger the loading rate the more the MSevents and ring number activities recodedThe figure for thisis 9762 and 13971 to 87517 in ring number and 325834 to 4430in MS single event Furthermore similar result for cementand glass sample can be found regarding the variation ofloading rate We believe that this is because MS phenomenonis actually kind of stress wave and release of energy and largerloading speed results in more rapid energy accumulating and

releasing outThus there aremoreMS events and ring countswere founded during the whole loading process

55 Relationship between MS Signal and Coal Powder ParticleSize A group of coal specimens (CSA2 CSB2 and CSC2)is selected to analyze the effect of coal powder size on MScharacteristics As shown in Table 1 under the same loadingrate (10Ns) the peak values are 774 kN 272 kN and 318 kNfor specimens made by particles size in 0ndash025mm 025ndash05mm and 05ndash2mm Theoretically the smaller the size ofpowders used is the larger the strength of pressured briquettesamples should be for small particles could bond wellAlthough CSB2 which was made up of 025ndash05mm particlesize powders only got 272 kN loading peak value before theprimary damage the general trend between CSA2 and CSC2showed an obvious decrease of sample strength with powdersize going up As bigger strength means more loading time itis not surprising thatmoreMS events were recorded for CSA2and CSC2 (Table 2) On the contrary CSB2 has the largestevent rate and it is 309 events every second as shown inTable 3 Another feature by which powder size will influencethe man-made specimen structure is the homogeneity Thisis because specimens made by larger particle size of powderhave more cracks inside As a result it will take longer time

Shock and Vibration 11

during the crack closure stage and finally get into linear elasticstage Also as shown in Figure 7 the curves of MS eventaccumulation with loading stage for CSA2 and CSB2 increasemore linearly while for CSC2 made by largest powder itis quite different In summary although the loading timespecimen strength and events statistic for CSB2 show someabnormality comparedwith general trend from small powdersize to large powder size made specimen the global eventaccumulation has great consistency with theoretical analysis

6 Conclusion

A total of 11 experiment tests on coal cement paste andglass specimens were carried out under uniaxial compressioncondition Coal powder particle size material type andloading rate are selected to compare and analyzeMSAE char-acteristic In the meantime the MS event rate accumulatedringing number relationship between loading stress and MSevent energy distribution and frequency-time characteris-tics of MS signal produced during the damage of differentspecimens were also discussed in the work

To separate the MS signal from the noises pollutedsignals the combination of wavelet packet transform (WPT)and local mean decomposition (LMD)methodwas chosen todenoise and analyze all the signals collected The applicationof this new method proved that the WPT-LMD method hada good performance in denoising and filtering the MS dataand it also could be used as common tool in the issues offrequency-spectrum characteristics analysis of MS signal orMS-like nonstationary and nonlinear signal

The investigation result on theMS signal features of threecategories of materials showed the following

(1) In terms of the damage and deformation of coal andcement in the early stage few effective signals areproduced then specimens will experience a relativeclam middle stage and in the poststage where mainrupture occurred a large number of MS events wererecorded But for glass samples theMS event genera-tions were throughout the whole loading process Weconclude that this is because of the brittle and crispyfeatures of glass material which means less energystored and quickly released

(2) Also there is good corresponding relation betweenloading time and MS events and ring numbers thelonger loading time is the more MS events and ringnumbers are detected while for loading rate there isno obvious effect on MS

(3) In the case of relationship for coal powder par-ticle size and MS feature we found that the MSaccumulation patterns of small particles (0ndash05mm)made specimens be more linear while large particles(05ndash2mm) made specimens own typical nonlinearcharacteristics The latter will spend much more timeto close the crack and be crushed into small powdersThere seems to be a decrease trend of specimenstrengths with the powder size used from small tolarge but there is abnormal case of CSB2 Similar

conclusion could be made for event and ring numbercounts and event rate this may be caused by a failureof specimen preparation and this is supposed to beexamined in the further study

(4) From the frequency-spectrum point of view all testson differentmaterials showedwide frequency domaincharacteristics which are fallen in the domain of0ndash200Hz 300ndash500Hz and 1000ndash1500Hz Howeverit should be noted that the frequency domain ofglass specimens is mainly concentrated in the lowfrequency (0ndash938Hz) due to their extremely brittlemechanical characteristics

To sum up we believe that the outcome of the paperwill be of great significance to improve the dynamic disastersforecast and improve coal mining safety This may embodysome promotions in data processing by using theWPT-LMDmethod and provide a theoretical basis for understandingthe mechanism of MS activities due to the experimentalphenomenon and conclusions presented

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This study was supported by National Natural Science Foun-dation of China (Grant no 51274206) The correspondingauthor also acknowledged the financial support provided byChinese Scholarship Council (CSC)

References

[1] C U Grosse andM Ohtsu Acoustic Emission Testing SpringerBerlin Germany 2008

[2] H RHardy JrAcoustic EmissionMicroseismic Activity Volume1 Principles Techniques and Geotechnical Applications CRCPress 2005

[3] C-P Lu L-M Dou B Liu Y-S Xie and H-S Liu ldquoMicroseis-mic low-frequency precursor effect of bursting failure of coaland rockrdquo Journal of Applied Geophysics vol 79 pp 55ndash63 2012

[4] C-P Lu L-M Dou H Liu H-S Liu B Liu and B-B DuldquoCase study on microseismic effect of coal and gas outburstprocessrdquo International Journal of Rock Mechanics and MiningSciences vol 53 pp 101ndash110 2012

[5] S I Qin J Cheng and S Zhu ldquoThe application and prospect ofmicroseismic technique in coalminerdquo Procedia EnvironmentalSciences vol 12 pp 218ndash224 2012

[6] X H Liu C X Liu S Zhuang L Wang and S S LinldquoLaboratory studies on acoustic emission characteristics to coaldynamic response under variable accelerative loadrdquo AppliedMechanics and Materials vol 580ndash583 pp 623ndash627 2014

[7] N Cook ldquoThe seismic location of rockburstsrdquo in Proceedings ofthe 5th Rock Mechanics Symposium pp 463ndash516 1963

[8] A G Beattie ldquoAcoustic emission principles and instrumenta-tionrdquo Journal of Acoustic Emission vol 2 no 12 pp 95ndash1281983

12 Shock and Vibration

[9] L Obert ldquoThe microseismic method discovery and earlyhistoryrdquo in Proceedings of the 1st Conference on Acoustic Emis-sionMicroseismic Activity in Geologic Structures and Materialspp 11ndash12 1977

[10] I Yamada K Masuda and H Mizutani ldquoElectromagnetic andacoustic emission associated with rock fracturerdquo Physics of theEarth and Planetary Interiors vol 57 no 1-2 pp 157ndash168 1989

[11] C-P Lu L-M Dou N Zhang et al ldquoMicroseismic frequency-spectrum evolutionary rule of rockburst triggered by roof fallrdquoInternational Journal of Rock Mechanics and Mining Sciencesvol 64 pp 6ndash16 2013

[12] S Sasaki ldquoCharacteristics of microseismic events inducedduring hydraulic fracturing experiments at the Hijiori hot dryrock geothermal energy site Yamagata Japanrdquo Tectonophysicsvol 289 no 1ndash3 pp 171ndash188 1998

[13] F Spaepen ldquoAmicroscopicmechanism for steady state inhomo-geneous flow in metallic glassesrdquo Acta Metallurgica vol 25 no4 pp 407ndash415 1977

[14] P E Tikkanen ldquoNonlinear wavelet and wavelet packet denois-ing of electrocardiogram signalrdquo Biological Cybernetics vol 80no 4 pp 259ndash267 1999

[15] O A M Aly A S Omar and A Z Elsherbeni ldquoDetectionand localization of RF radar pulses in noise environments usingwavelet packet transform and higher order statisticsrdquo Progressin Electromagnetics Research vol 58 pp 301ndash317 2006

[16] A Kyprianou P L Lewin V Efthimiou A Stavrou and GE Georghiou ldquoWavelet packet denoising for online partialdischarge detection in cables and its application to experimentalfield resultsrdquoMeasurement Science and Technology vol 17 no 9pp 2367ndash2379 2006

[17] J S Smith ldquoThe local mean decomposition and its applicationto EEG perception datardquo Journal of the Royal Society Interfacevol 2 no 5 pp 443ndash454 2005

[18] Z Sun and C C Chang ldquoStructural damage assessment basedon wavelet packet transformrdquo Journal of Structural Engineeringvol 128 no 10 pp 1354ndash1361 2002

[19] J-D Wu and C-H Liu ldquoAn expert system for fault diagnosisin internal combustion engines using wavelet packet transformand neural networkrdquo Expert Systems with Applications vol 36no 3 pp 4278ndash4286 2009

[20] Y Wang Z He and Y Zi ldquoA demodulation method based onimproved local mean decomposition and its application in rub-impact fault diagnosisrdquo Measurement Science and Technologyvol 20 no 2 Article ID 025704 2009

[21] Y Wang Z He and Y Zi ldquoA comparative study on thelocal mean decomposition and empirical mode decompositionand their applications to rotating machinery health diagnosisrdquoJournal of Vibration and Acoustics vol 132 no 2 Article ID021010 2010

[22] A Trnkoczy ldquoUnderstanding and parameter setting ofSTALTA trigger algorithmrdquo in IASPEI New Manual ofSeismological Observatory Practice vol 2 pp 1ndash19 DeutschesGeoForschungsZentrum GFZ Potsdam Germany 2002

[23] V Vishal P G Ranjith and T N Singh ldquoAn experimentalinvestigation on behaviour of coal under fluid saturationusing acoustic emissionrdquo Journal of Natural Gas Science andEngineering vol 22 pp 428ndash436 2015

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Shock and Vibration 7

100 200 3000Time (s)

minus005

0

005

PF-5

(mV

)

minus02

0

02

PF-4

(mV

)

100 200 3000Time (s)

100 200 3000Time (s)

minus05

0

05

PF-3

(mV

)

100 200 3000Time (s)

minus5

0

5

PF-2

(mV

)

minus10

0

10

PF-1

(mV

)

100 200 3000Time (s)

100 200 3000Time (s)

minus10

0

10

MS

(mV

)times10

minus3

0

1

2

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus3

0

1

2

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus4

500 1000 15000Frequency (Hz)

0

2

4

Am

plitu

de

times10minus4

0

05

1

Am

plitu

de

500 1000 15000Frequency (Hz)

times10minus4

500 1000 15000Frequency (Hz)

0

05

1

Am

plitu

de

times10minus5

500 1000 15000Frequency (Hz)

0

5

Am

plitu

de

Figure 5 A typical LMD result of CSC2

sections of typicalMS events data of cement sample (CMSA1)and glass sample (GSB1) in early stage middle stage andpoststage In terms of cement specimen the maximumamplitude of signal increases obviously with the loadingstage the real amplitude from 1mV in early stage to 20mVin the poststage and relative amplitude of FFT from 001 to004 From the frequency domain point of view before thecoming of main fracture in poststage the frequency of signalis mainly from 0 to 500Hz with maximum frequency around100ndash500Hz while in the poststage the main frequency isfrom 1000 to 1500Hz Also it should be noticed that in

every stage theMS signals all owned relativelywide frequencydomain distribution Analysis result of the same method oncoal specimen is very similar to cement However for glasssamples the maximum amplitude is not in the poststageand the frequency distribution is also different As shown inFigure 4 the MS events are detected in the whole loadingprocess Theoretically speaking coal and cement sampleswere similarly made up of powders and couplant to someextent these two kinds of samples are homogeneous andisotropic while glass samples are combination of severallayers This yields the difference of inter- or intragrain failure

8 Shock and Vibration

Original data (GSB1) Fast Fourier transform (GSB1)

(a)(a)

(b)

(c)(c)

(b)

Original data (CMSA1) Fast Fourier transform (CMSA1)

(a)

(b)

(c) (c)

(b)

(a)

minus1minus05

005

1

Am

plitu

de (m

V)

01 09 10 05 06 07 080302 04Time (s)

01 02 03 04 05 06 07 08 09 10Time (s)

minus1minus05

005

1

Am

plitu

de (m

V)

minus20minus10

01020

Am

plitu

de (m

V)

01 09 10 05 06 07 080302 04Time (s)

minus20

minus10

0

10

20

Am

plitu

de (m

V)

01 02 03 04 05 06 07 08 09 10Time (s)

01 02 03 04 05 06 07 08 09 10Time (s)

minus20

minus10

0

10

20

Am

plitu

de (m

V)

01 02 03 04 05 06 07 08 09 10Time (s)

minus5

0

5

Am

plitu

de (m

V)

500 1000 15000Frequency (Hz)

00002000400060008

001

Am

plitu

de

500 1000 15000Frequency (Hz)

0

005

01

015

02

Am

plitu

de

500 1000 15000Frequency (Hz)

0

01

02

03

04

Am

plitu

de

500 1000 15000Frequency (Hz)

0001002003004

Am

plitu

de

500 1000 15000Frequency (Hz)

00005

0010015

002

Am

plitu

de

500 1000 15000Frequency (Hz)

00002000400060008

001

Am

plitu

de

Figure 6 MS signal during cement and glass damage in different loading stages (a) early stage (b) middle stage (c) poststage

Shock and Vibration 9

CSA2CSB2CSC2CMSA2GSB1GSB3

CSB3CSB1

CMSA1CMSA3GSB2

Loading stage

100

80

60

40

20

0

MS

even

t acc

umul

atio

n (

)

I II III IV V VI VII VIII IX X

Figure 7 The event accumulation variation during the loadingstage

and the rule of crack or cleat initiation and propagation inthe samples Furthermore the intrinsic resonant frequenciesof variations of materials also contribute to the monitoringresult

52MS Event Count and Event Rate Asmentioned above weused the STALTA method to recognize effective MS eventcounting the ring and event number During the detection ofeffective MS signal if the ratio of STA and LTA is over thethreshold the number counter of MS event and ring will betriggered but the difference is that a single event will not stopuntil the value goes back to being under the threshold whilethe ring number counts all single section over the criticalamplitude It can be seen from the study that the MS eventand ring count are quite different for these three kinds ofmaterials and this shows irregular characteristics even inthe same kind of material (Tables 2 and 3) An interestingphenomenon is that cement specimen procedures least MSevent in comparison with coal and glass However as theloading time for each sample is quite different the eventrate value is quite similar for all the samples The averageevent rate per second is around 2 but for cement sample likeCMSA1 and CMSA3 it is only 025 and 022

Figure 7 shows the total event accumulation ratio of thewhole loading process The variations tendencies are quitedifferent for different sample for one group of the samples(CSB2 CSA2 CSB3 GSB1 and GSB3) shows linear type ofincrease in the loading process while for the other seriesof samples (CMSA1 GSB2 CMSA2 and CMSA3) eventaccumulation increases smoothly at first (stages IndashV) andgrows sharply after that (stages VIndashX) These two types oftendencies give an illustration of MS activities in the whole

Specimen number

dc1dc2dc3dc4

dc5dc6dc7dc8

CSA

2

CMSA

2

GSB

1

GSB

3

GSB

2

CSB3

CSB1

CMSA

1

CMSA

3

00

02

04

06

08

10

Ener

gy p

ropo

rtio

n (

)

Figure 8 Energy proportion of WPT best tree decomposition

process and this phenomenon has great agreement with thestress and material damage rules

53The Energy Distribution Characteristics of MS Signal Theanalysis on energy distribution ofMS signal is based on resultofWPT best tree transformation It can be seen from Figure 1that the signal is decomposed into 8 decompositions denotedas dc1 to dc8 According to the node number the corre-sponding frequency bands of each node component are 0ndash469Hz 469ndash938Hz 938ndash1406Hz 1406ndash1875Hz 1875ndash3750Hz 3750ndash750Hz 750ndash10781Hz and 10781ndash1500Hzrespectively Examples of several specimens in terms ofenergy distribution of the eight decompositions are shownin Figure 8 Hereby the energy distribution analysis alsoshows the frequency-spectrum characteristics of differentmaterials It is obvious to see and conclude that all specimensmade of different materials are abundant with low frequencycomponents especially for dc1 However some differencesbetween each material type could also be found In the casesof glass specimens the total signal energy for each specimenin 0ndash469Hz is all more than 50 (629 958 and 507)As glass is a kind of typical brittle material there are alwaysbig fractures generated directly when it is compressed anddeformed This kind of rapid release of energy will limitthe energy accumulation and the big fractures all limit thespreading of stress wave Consequently the signal energyis mainly from low frequency domain By contrast theenergy proportions for cement produced signals are widelydistributed in all frequency domains mainly covering the 0ndash938Hz and 1406ndash750Hz But for coal the situation is muchmore complicated The main energy distribution domain forevery sample seems to change greatly in their medium and

10 Shock and Vibration

Table 2 Ring count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X TotalCSA2 6367 7147 8068 5044 4966 4792 3178 4998 2793 11400 58753CSB1 194 1666 892 140 59 22 71 174 2399 4145 9762CSB2 1797 1938 1430 1264 1746 1117 814 1363 932 1570 13971CSB3 13174 6847 8510 8543 6849 7595 8019 10858 7705 9417 87517CSC2 1596 6451 3153 569 640 123 0 122 177 3259 16090CMSA1 630 57 43 0 905 567 238 686 707 1686 5519CMSA2 243 0 152 25 482 211 362 597 1146 2350 5568CMSA3 1652 325 199 175 667 1203 1563 3438 11684 11132 32038GSB1 6961 5387 7107 4334 3437 4303 3160 2680 3460 1507 42336GSB2 2016 2521 2578 2613 3802 6703 17618 23977 23964 21802 107594GSB3 11958 9097 10882 16121 11292 9896 10314 11962 18636 17162 127320

Table 3 MS event count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X Total Event rateCSA2 311 294 302 219 239 226 159 238 121 368 2477 236CSB1 23 57 27 4 2 1 3 6 84 118 325 217CSB2 94 111 93 78 101 71 61 81 67 77 834 309CSB3 559 409 428 472 359 405 413 519 388 478 4430 242CSC2 83 132 84 14 11 3 0 2 4 87 420 14CMSA1 10 0 6 2 4 6 17 21 36 81 183 022CMSA2 28 3 2 0 37 21 10 36 36 73 246 025CMSA3 66 34 20 21 27 67 66 108 374 333 1116 13GSB1 220 162 187 133 76 117 103 91 96 58 1243 143GSB2 100 92 107 90 176 268 531 609 602 603 3178 195GSB3 401 323 382 505 378 335 342 417 540 535 4158 258

high frequency components Specifically CSA2 has morefrequency components for d8 and CSB1 has more for d5while CSB3 is abundant with d7 This may be mainly becauseof the differences for loading rate and coal powder sizes andof course the boundaries and interfaces inside the samples

54 Relationship between MS Signal and Loading Rate Toanalyze the microseismic characteristics of different materi-als different loading rate is set in the process of experimentAs mentioned above coal cement paste and glass speci-mens have quite different mechanic strength and materialcharacteristics Thus we set different loading rate variationfor each type of materials for coal it is 5ndash20Ns and forcement and glass it is 150ndash300Ns Taking coal samples asan example the statistics on CSB1 CSB2 and CSB3 showthat the loading peak force values increase as the loading rateset larger and the bigger the loading rate the more the MSevents and ring number activities recodedThe figure for thisis 9762 and 13971 to 87517 in ring number and 325834 to 4430in MS single event Furthermore similar result for cementand glass sample can be found regarding the variation ofloading rate We believe that this is because MS phenomenonis actually kind of stress wave and release of energy and largerloading speed results in more rapid energy accumulating and

releasing outThus there aremoreMS events and ring countswere founded during the whole loading process

55 Relationship between MS Signal and Coal Powder ParticleSize A group of coal specimens (CSA2 CSB2 and CSC2)is selected to analyze the effect of coal powder size on MScharacteristics As shown in Table 1 under the same loadingrate (10Ns) the peak values are 774 kN 272 kN and 318 kNfor specimens made by particles size in 0ndash025mm 025ndash05mm and 05ndash2mm Theoretically the smaller the size ofpowders used is the larger the strength of pressured briquettesamples should be for small particles could bond wellAlthough CSB2 which was made up of 025ndash05mm particlesize powders only got 272 kN loading peak value before theprimary damage the general trend between CSA2 and CSC2showed an obvious decrease of sample strength with powdersize going up As bigger strength means more loading time itis not surprising thatmoreMS events were recorded for CSA2and CSC2 (Table 2) On the contrary CSB2 has the largestevent rate and it is 309 events every second as shown inTable 3 Another feature by which powder size will influencethe man-made specimen structure is the homogeneity Thisis because specimens made by larger particle size of powderhave more cracks inside As a result it will take longer time

Shock and Vibration 11

during the crack closure stage and finally get into linear elasticstage Also as shown in Figure 7 the curves of MS eventaccumulation with loading stage for CSA2 and CSB2 increasemore linearly while for CSC2 made by largest powder itis quite different In summary although the loading timespecimen strength and events statistic for CSB2 show someabnormality comparedwith general trend from small powdersize to large powder size made specimen the global eventaccumulation has great consistency with theoretical analysis

6 Conclusion

A total of 11 experiment tests on coal cement paste andglass specimens were carried out under uniaxial compressioncondition Coal powder particle size material type andloading rate are selected to compare and analyzeMSAE char-acteristic In the meantime the MS event rate accumulatedringing number relationship between loading stress and MSevent energy distribution and frequency-time characteris-tics of MS signal produced during the damage of differentspecimens were also discussed in the work

To separate the MS signal from the noises pollutedsignals the combination of wavelet packet transform (WPT)and local mean decomposition (LMD)methodwas chosen todenoise and analyze all the signals collected The applicationof this new method proved that the WPT-LMD method hada good performance in denoising and filtering the MS dataand it also could be used as common tool in the issues offrequency-spectrum characteristics analysis of MS signal orMS-like nonstationary and nonlinear signal

The investigation result on theMS signal features of threecategories of materials showed the following

(1) In terms of the damage and deformation of coal andcement in the early stage few effective signals areproduced then specimens will experience a relativeclam middle stage and in the poststage where mainrupture occurred a large number of MS events wererecorded But for glass samples theMS event genera-tions were throughout the whole loading process Weconclude that this is because of the brittle and crispyfeatures of glass material which means less energystored and quickly released

(2) Also there is good corresponding relation betweenloading time and MS events and ring numbers thelonger loading time is the more MS events and ringnumbers are detected while for loading rate there isno obvious effect on MS

(3) In the case of relationship for coal powder par-ticle size and MS feature we found that the MSaccumulation patterns of small particles (0ndash05mm)made specimens be more linear while large particles(05ndash2mm) made specimens own typical nonlinearcharacteristics The latter will spend much more timeto close the crack and be crushed into small powdersThere seems to be a decrease trend of specimenstrengths with the powder size used from small tolarge but there is abnormal case of CSB2 Similar

conclusion could be made for event and ring numbercounts and event rate this may be caused by a failureof specimen preparation and this is supposed to beexamined in the further study

(4) From the frequency-spectrum point of view all testson differentmaterials showedwide frequency domaincharacteristics which are fallen in the domain of0ndash200Hz 300ndash500Hz and 1000ndash1500Hz Howeverit should be noted that the frequency domain ofglass specimens is mainly concentrated in the lowfrequency (0ndash938Hz) due to their extremely brittlemechanical characteristics

To sum up we believe that the outcome of the paperwill be of great significance to improve the dynamic disastersforecast and improve coal mining safety This may embodysome promotions in data processing by using theWPT-LMDmethod and provide a theoretical basis for understandingthe mechanism of MS activities due to the experimentalphenomenon and conclusions presented

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This study was supported by National Natural Science Foun-dation of China (Grant no 51274206) The correspondingauthor also acknowledged the financial support provided byChinese Scholarship Council (CSC)

References

[1] C U Grosse andM Ohtsu Acoustic Emission Testing SpringerBerlin Germany 2008

[2] H RHardy JrAcoustic EmissionMicroseismic Activity Volume1 Principles Techniques and Geotechnical Applications CRCPress 2005

[3] C-P Lu L-M Dou B Liu Y-S Xie and H-S Liu ldquoMicroseis-mic low-frequency precursor effect of bursting failure of coaland rockrdquo Journal of Applied Geophysics vol 79 pp 55ndash63 2012

[4] C-P Lu L-M Dou H Liu H-S Liu B Liu and B-B DuldquoCase study on microseismic effect of coal and gas outburstprocessrdquo International Journal of Rock Mechanics and MiningSciences vol 53 pp 101ndash110 2012

[5] S I Qin J Cheng and S Zhu ldquoThe application and prospect ofmicroseismic technique in coalminerdquo Procedia EnvironmentalSciences vol 12 pp 218ndash224 2012

[6] X H Liu C X Liu S Zhuang L Wang and S S LinldquoLaboratory studies on acoustic emission characteristics to coaldynamic response under variable accelerative loadrdquo AppliedMechanics and Materials vol 580ndash583 pp 623ndash627 2014

[7] N Cook ldquoThe seismic location of rockburstsrdquo in Proceedings ofthe 5th Rock Mechanics Symposium pp 463ndash516 1963

[8] A G Beattie ldquoAcoustic emission principles and instrumenta-tionrdquo Journal of Acoustic Emission vol 2 no 12 pp 95ndash1281983

12 Shock and Vibration

[9] L Obert ldquoThe microseismic method discovery and earlyhistoryrdquo in Proceedings of the 1st Conference on Acoustic Emis-sionMicroseismic Activity in Geologic Structures and Materialspp 11ndash12 1977

[10] I Yamada K Masuda and H Mizutani ldquoElectromagnetic andacoustic emission associated with rock fracturerdquo Physics of theEarth and Planetary Interiors vol 57 no 1-2 pp 157ndash168 1989

[11] C-P Lu L-M Dou N Zhang et al ldquoMicroseismic frequency-spectrum evolutionary rule of rockburst triggered by roof fallrdquoInternational Journal of Rock Mechanics and Mining Sciencesvol 64 pp 6ndash16 2013

[12] S Sasaki ldquoCharacteristics of microseismic events inducedduring hydraulic fracturing experiments at the Hijiori hot dryrock geothermal energy site Yamagata Japanrdquo Tectonophysicsvol 289 no 1ndash3 pp 171ndash188 1998

[13] F Spaepen ldquoAmicroscopicmechanism for steady state inhomo-geneous flow in metallic glassesrdquo Acta Metallurgica vol 25 no4 pp 407ndash415 1977

[14] P E Tikkanen ldquoNonlinear wavelet and wavelet packet denois-ing of electrocardiogram signalrdquo Biological Cybernetics vol 80no 4 pp 259ndash267 1999

[15] O A M Aly A S Omar and A Z Elsherbeni ldquoDetectionand localization of RF radar pulses in noise environments usingwavelet packet transform and higher order statisticsrdquo Progressin Electromagnetics Research vol 58 pp 301ndash317 2006

[16] A Kyprianou P L Lewin V Efthimiou A Stavrou and GE Georghiou ldquoWavelet packet denoising for online partialdischarge detection in cables and its application to experimentalfield resultsrdquoMeasurement Science and Technology vol 17 no 9pp 2367ndash2379 2006

[17] J S Smith ldquoThe local mean decomposition and its applicationto EEG perception datardquo Journal of the Royal Society Interfacevol 2 no 5 pp 443ndash454 2005

[18] Z Sun and C C Chang ldquoStructural damage assessment basedon wavelet packet transformrdquo Journal of Structural Engineeringvol 128 no 10 pp 1354ndash1361 2002

[19] J-D Wu and C-H Liu ldquoAn expert system for fault diagnosisin internal combustion engines using wavelet packet transformand neural networkrdquo Expert Systems with Applications vol 36no 3 pp 4278ndash4286 2009

[20] Y Wang Z He and Y Zi ldquoA demodulation method based onimproved local mean decomposition and its application in rub-impact fault diagnosisrdquo Measurement Science and Technologyvol 20 no 2 Article ID 025704 2009

[21] Y Wang Z He and Y Zi ldquoA comparative study on thelocal mean decomposition and empirical mode decompositionand their applications to rotating machinery health diagnosisrdquoJournal of Vibration and Acoustics vol 132 no 2 Article ID021010 2010

[22] A Trnkoczy ldquoUnderstanding and parameter setting ofSTALTA trigger algorithmrdquo in IASPEI New Manual ofSeismological Observatory Practice vol 2 pp 1ndash19 DeutschesGeoForschungsZentrum GFZ Potsdam Germany 2002

[23] V Vishal P G Ranjith and T N Singh ldquoAn experimentalinvestigation on behaviour of coal under fluid saturationusing acoustic emissionrdquo Journal of Natural Gas Science andEngineering vol 22 pp 428ndash436 2015

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

8 Shock and Vibration

Original data (GSB1) Fast Fourier transform (GSB1)

(a)(a)

(b)

(c)(c)

(b)

Original data (CMSA1) Fast Fourier transform (CMSA1)

(a)

(b)

(c) (c)

(b)

(a)

minus1minus05

005

1

Am

plitu

de (m

V)

01 09 10 05 06 07 080302 04Time (s)

01 02 03 04 05 06 07 08 09 10Time (s)

minus1minus05

005

1

Am

plitu

de (m

V)

minus20minus10

01020

Am

plitu

de (m

V)

01 09 10 05 06 07 080302 04Time (s)

minus20

minus10

0

10

20

Am

plitu

de (m

V)

01 02 03 04 05 06 07 08 09 10Time (s)

01 02 03 04 05 06 07 08 09 10Time (s)

minus20

minus10

0

10

20

Am

plitu

de (m

V)

01 02 03 04 05 06 07 08 09 10Time (s)

minus5

0

5

Am

plitu

de (m

V)

500 1000 15000Frequency (Hz)

00002000400060008

001

Am

plitu

de

500 1000 15000Frequency (Hz)

0

005

01

015

02

Am

plitu

de

500 1000 15000Frequency (Hz)

0

01

02

03

04

Am

plitu

de

500 1000 15000Frequency (Hz)

0001002003004

Am

plitu

de

500 1000 15000Frequency (Hz)

00005

0010015

002

Am

plitu

de

500 1000 15000Frequency (Hz)

00002000400060008

001

Am

plitu

de

Figure 6 MS signal during cement and glass damage in different loading stages (a) early stage (b) middle stage (c) poststage

Shock and Vibration 9

CSA2CSB2CSC2CMSA2GSB1GSB3

CSB3CSB1

CMSA1CMSA3GSB2

Loading stage

100

80

60

40

20

0

MS

even

t acc

umul

atio

n (

)

I II III IV V VI VII VIII IX X

Figure 7 The event accumulation variation during the loadingstage

and the rule of crack or cleat initiation and propagation inthe samples Furthermore the intrinsic resonant frequenciesof variations of materials also contribute to the monitoringresult

52MS Event Count and Event Rate Asmentioned above weused the STALTA method to recognize effective MS eventcounting the ring and event number During the detection ofeffective MS signal if the ratio of STA and LTA is over thethreshold the number counter of MS event and ring will betriggered but the difference is that a single event will not stopuntil the value goes back to being under the threshold whilethe ring number counts all single section over the criticalamplitude It can be seen from the study that the MS eventand ring count are quite different for these three kinds ofmaterials and this shows irregular characteristics even inthe same kind of material (Tables 2 and 3) An interestingphenomenon is that cement specimen procedures least MSevent in comparison with coal and glass However as theloading time for each sample is quite different the eventrate value is quite similar for all the samples The averageevent rate per second is around 2 but for cement sample likeCMSA1 and CMSA3 it is only 025 and 022

Figure 7 shows the total event accumulation ratio of thewhole loading process The variations tendencies are quitedifferent for different sample for one group of the samples(CSB2 CSA2 CSB3 GSB1 and GSB3) shows linear type ofincrease in the loading process while for the other seriesof samples (CMSA1 GSB2 CMSA2 and CMSA3) eventaccumulation increases smoothly at first (stages IndashV) andgrows sharply after that (stages VIndashX) These two types oftendencies give an illustration of MS activities in the whole

Specimen number

dc1dc2dc3dc4

dc5dc6dc7dc8

CSA

2

CMSA

2

GSB

1

GSB

3

GSB

2

CSB3

CSB1

CMSA

1

CMSA

3

00

02

04

06

08

10

Ener

gy p

ropo

rtio

n (

)

Figure 8 Energy proportion of WPT best tree decomposition

process and this phenomenon has great agreement with thestress and material damage rules

53The Energy Distribution Characteristics of MS Signal Theanalysis on energy distribution ofMS signal is based on resultofWPT best tree transformation It can be seen from Figure 1that the signal is decomposed into 8 decompositions denotedas dc1 to dc8 According to the node number the corre-sponding frequency bands of each node component are 0ndash469Hz 469ndash938Hz 938ndash1406Hz 1406ndash1875Hz 1875ndash3750Hz 3750ndash750Hz 750ndash10781Hz and 10781ndash1500Hzrespectively Examples of several specimens in terms ofenergy distribution of the eight decompositions are shownin Figure 8 Hereby the energy distribution analysis alsoshows the frequency-spectrum characteristics of differentmaterials It is obvious to see and conclude that all specimensmade of different materials are abundant with low frequencycomponents especially for dc1 However some differencesbetween each material type could also be found In the casesof glass specimens the total signal energy for each specimenin 0ndash469Hz is all more than 50 (629 958 and 507)As glass is a kind of typical brittle material there are alwaysbig fractures generated directly when it is compressed anddeformed This kind of rapid release of energy will limitthe energy accumulation and the big fractures all limit thespreading of stress wave Consequently the signal energyis mainly from low frequency domain By contrast theenergy proportions for cement produced signals are widelydistributed in all frequency domains mainly covering the 0ndash938Hz and 1406ndash750Hz But for coal the situation is muchmore complicated The main energy distribution domain forevery sample seems to change greatly in their medium and

10 Shock and Vibration

Table 2 Ring count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X TotalCSA2 6367 7147 8068 5044 4966 4792 3178 4998 2793 11400 58753CSB1 194 1666 892 140 59 22 71 174 2399 4145 9762CSB2 1797 1938 1430 1264 1746 1117 814 1363 932 1570 13971CSB3 13174 6847 8510 8543 6849 7595 8019 10858 7705 9417 87517CSC2 1596 6451 3153 569 640 123 0 122 177 3259 16090CMSA1 630 57 43 0 905 567 238 686 707 1686 5519CMSA2 243 0 152 25 482 211 362 597 1146 2350 5568CMSA3 1652 325 199 175 667 1203 1563 3438 11684 11132 32038GSB1 6961 5387 7107 4334 3437 4303 3160 2680 3460 1507 42336GSB2 2016 2521 2578 2613 3802 6703 17618 23977 23964 21802 107594GSB3 11958 9097 10882 16121 11292 9896 10314 11962 18636 17162 127320

Table 3 MS event count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X Total Event rateCSA2 311 294 302 219 239 226 159 238 121 368 2477 236CSB1 23 57 27 4 2 1 3 6 84 118 325 217CSB2 94 111 93 78 101 71 61 81 67 77 834 309CSB3 559 409 428 472 359 405 413 519 388 478 4430 242CSC2 83 132 84 14 11 3 0 2 4 87 420 14CMSA1 10 0 6 2 4 6 17 21 36 81 183 022CMSA2 28 3 2 0 37 21 10 36 36 73 246 025CMSA3 66 34 20 21 27 67 66 108 374 333 1116 13GSB1 220 162 187 133 76 117 103 91 96 58 1243 143GSB2 100 92 107 90 176 268 531 609 602 603 3178 195GSB3 401 323 382 505 378 335 342 417 540 535 4158 258

high frequency components Specifically CSA2 has morefrequency components for d8 and CSB1 has more for d5while CSB3 is abundant with d7 This may be mainly becauseof the differences for loading rate and coal powder sizes andof course the boundaries and interfaces inside the samples

54 Relationship between MS Signal and Loading Rate Toanalyze the microseismic characteristics of different materi-als different loading rate is set in the process of experimentAs mentioned above coal cement paste and glass speci-mens have quite different mechanic strength and materialcharacteristics Thus we set different loading rate variationfor each type of materials for coal it is 5ndash20Ns and forcement and glass it is 150ndash300Ns Taking coal samples asan example the statistics on CSB1 CSB2 and CSB3 showthat the loading peak force values increase as the loading rateset larger and the bigger the loading rate the more the MSevents and ring number activities recodedThe figure for thisis 9762 and 13971 to 87517 in ring number and 325834 to 4430in MS single event Furthermore similar result for cementand glass sample can be found regarding the variation ofloading rate We believe that this is because MS phenomenonis actually kind of stress wave and release of energy and largerloading speed results in more rapid energy accumulating and

releasing outThus there aremoreMS events and ring countswere founded during the whole loading process

55 Relationship between MS Signal and Coal Powder ParticleSize A group of coal specimens (CSA2 CSB2 and CSC2)is selected to analyze the effect of coal powder size on MScharacteristics As shown in Table 1 under the same loadingrate (10Ns) the peak values are 774 kN 272 kN and 318 kNfor specimens made by particles size in 0ndash025mm 025ndash05mm and 05ndash2mm Theoretically the smaller the size ofpowders used is the larger the strength of pressured briquettesamples should be for small particles could bond wellAlthough CSB2 which was made up of 025ndash05mm particlesize powders only got 272 kN loading peak value before theprimary damage the general trend between CSA2 and CSC2showed an obvious decrease of sample strength with powdersize going up As bigger strength means more loading time itis not surprising thatmoreMS events were recorded for CSA2and CSC2 (Table 2) On the contrary CSB2 has the largestevent rate and it is 309 events every second as shown inTable 3 Another feature by which powder size will influencethe man-made specimen structure is the homogeneity Thisis because specimens made by larger particle size of powderhave more cracks inside As a result it will take longer time

Shock and Vibration 11

during the crack closure stage and finally get into linear elasticstage Also as shown in Figure 7 the curves of MS eventaccumulation with loading stage for CSA2 and CSB2 increasemore linearly while for CSC2 made by largest powder itis quite different In summary although the loading timespecimen strength and events statistic for CSB2 show someabnormality comparedwith general trend from small powdersize to large powder size made specimen the global eventaccumulation has great consistency with theoretical analysis

6 Conclusion

A total of 11 experiment tests on coal cement paste andglass specimens were carried out under uniaxial compressioncondition Coal powder particle size material type andloading rate are selected to compare and analyzeMSAE char-acteristic In the meantime the MS event rate accumulatedringing number relationship between loading stress and MSevent energy distribution and frequency-time characteris-tics of MS signal produced during the damage of differentspecimens were also discussed in the work

To separate the MS signal from the noises pollutedsignals the combination of wavelet packet transform (WPT)and local mean decomposition (LMD)methodwas chosen todenoise and analyze all the signals collected The applicationof this new method proved that the WPT-LMD method hada good performance in denoising and filtering the MS dataand it also could be used as common tool in the issues offrequency-spectrum characteristics analysis of MS signal orMS-like nonstationary and nonlinear signal

The investigation result on theMS signal features of threecategories of materials showed the following

(1) In terms of the damage and deformation of coal andcement in the early stage few effective signals areproduced then specimens will experience a relativeclam middle stage and in the poststage where mainrupture occurred a large number of MS events wererecorded But for glass samples theMS event genera-tions were throughout the whole loading process Weconclude that this is because of the brittle and crispyfeatures of glass material which means less energystored and quickly released

(2) Also there is good corresponding relation betweenloading time and MS events and ring numbers thelonger loading time is the more MS events and ringnumbers are detected while for loading rate there isno obvious effect on MS

(3) In the case of relationship for coal powder par-ticle size and MS feature we found that the MSaccumulation patterns of small particles (0ndash05mm)made specimens be more linear while large particles(05ndash2mm) made specimens own typical nonlinearcharacteristics The latter will spend much more timeto close the crack and be crushed into small powdersThere seems to be a decrease trend of specimenstrengths with the powder size used from small tolarge but there is abnormal case of CSB2 Similar

conclusion could be made for event and ring numbercounts and event rate this may be caused by a failureof specimen preparation and this is supposed to beexamined in the further study

(4) From the frequency-spectrum point of view all testson differentmaterials showedwide frequency domaincharacteristics which are fallen in the domain of0ndash200Hz 300ndash500Hz and 1000ndash1500Hz Howeverit should be noted that the frequency domain ofglass specimens is mainly concentrated in the lowfrequency (0ndash938Hz) due to their extremely brittlemechanical characteristics

To sum up we believe that the outcome of the paperwill be of great significance to improve the dynamic disastersforecast and improve coal mining safety This may embodysome promotions in data processing by using theWPT-LMDmethod and provide a theoretical basis for understandingthe mechanism of MS activities due to the experimentalphenomenon and conclusions presented

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This study was supported by National Natural Science Foun-dation of China (Grant no 51274206) The correspondingauthor also acknowledged the financial support provided byChinese Scholarship Council (CSC)

References

[1] C U Grosse andM Ohtsu Acoustic Emission Testing SpringerBerlin Germany 2008

[2] H RHardy JrAcoustic EmissionMicroseismic Activity Volume1 Principles Techniques and Geotechnical Applications CRCPress 2005

[3] C-P Lu L-M Dou B Liu Y-S Xie and H-S Liu ldquoMicroseis-mic low-frequency precursor effect of bursting failure of coaland rockrdquo Journal of Applied Geophysics vol 79 pp 55ndash63 2012

[4] C-P Lu L-M Dou H Liu H-S Liu B Liu and B-B DuldquoCase study on microseismic effect of coal and gas outburstprocessrdquo International Journal of Rock Mechanics and MiningSciences vol 53 pp 101ndash110 2012

[5] S I Qin J Cheng and S Zhu ldquoThe application and prospect ofmicroseismic technique in coalminerdquo Procedia EnvironmentalSciences vol 12 pp 218ndash224 2012

[6] X H Liu C X Liu S Zhuang L Wang and S S LinldquoLaboratory studies on acoustic emission characteristics to coaldynamic response under variable accelerative loadrdquo AppliedMechanics and Materials vol 580ndash583 pp 623ndash627 2014

[7] N Cook ldquoThe seismic location of rockburstsrdquo in Proceedings ofthe 5th Rock Mechanics Symposium pp 463ndash516 1963

[8] A G Beattie ldquoAcoustic emission principles and instrumenta-tionrdquo Journal of Acoustic Emission vol 2 no 12 pp 95ndash1281983

12 Shock and Vibration

[9] L Obert ldquoThe microseismic method discovery and earlyhistoryrdquo in Proceedings of the 1st Conference on Acoustic Emis-sionMicroseismic Activity in Geologic Structures and Materialspp 11ndash12 1977

[10] I Yamada K Masuda and H Mizutani ldquoElectromagnetic andacoustic emission associated with rock fracturerdquo Physics of theEarth and Planetary Interiors vol 57 no 1-2 pp 157ndash168 1989

[11] C-P Lu L-M Dou N Zhang et al ldquoMicroseismic frequency-spectrum evolutionary rule of rockburst triggered by roof fallrdquoInternational Journal of Rock Mechanics and Mining Sciencesvol 64 pp 6ndash16 2013

[12] S Sasaki ldquoCharacteristics of microseismic events inducedduring hydraulic fracturing experiments at the Hijiori hot dryrock geothermal energy site Yamagata Japanrdquo Tectonophysicsvol 289 no 1ndash3 pp 171ndash188 1998

[13] F Spaepen ldquoAmicroscopicmechanism for steady state inhomo-geneous flow in metallic glassesrdquo Acta Metallurgica vol 25 no4 pp 407ndash415 1977

[14] P E Tikkanen ldquoNonlinear wavelet and wavelet packet denois-ing of electrocardiogram signalrdquo Biological Cybernetics vol 80no 4 pp 259ndash267 1999

[15] O A M Aly A S Omar and A Z Elsherbeni ldquoDetectionand localization of RF radar pulses in noise environments usingwavelet packet transform and higher order statisticsrdquo Progressin Electromagnetics Research vol 58 pp 301ndash317 2006

[16] A Kyprianou P L Lewin V Efthimiou A Stavrou and GE Georghiou ldquoWavelet packet denoising for online partialdischarge detection in cables and its application to experimentalfield resultsrdquoMeasurement Science and Technology vol 17 no 9pp 2367ndash2379 2006

[17] J S Smith ldquoThe local mean decomposition and its applicationto EEG perception datardquo Journal of the Royal Society Interfacevol 2 no 5 pp 443ndash454 2005

[18] Z Sun and C C Chang ldquoStructural damage assessment basedon wavelet packet transformrdquo Journal of Structural Engineeringvol 128 no 10 pp 1354ndash1361 2002

[19] J-D Wu and C-H Liu ldquoAn expert system for fault diagnosisin internal combustion engines using wavelet packet transformand neural networkrdquo Expert Systems with Applications vol 36no 3 pp 4278ndash4286 2009

[20] Y Wang Z He and Y Zi ldquoA demodulation method based onimproved local mean decomposition and its application in rub-impact fault diagnosisrdquo Measurement Science and Technologyvol 20 no 2 Article ID 025704 2009

[21] Y Wang Z He and Y Zi ldquoA comparative study on thelocal mean decomposition and empirical mode decompositionand their applications to rotating machinery health diagnosisrdquoJournal of Vibration and Acoustics vol 132 no 2 Article ID021010 2010

[22] A Trnkoczy ldquoUnderstanding and parameter setting ofSTALTA trigger algorithmrdquo in IASPEI New Manual ofSeismological Observatory Practice vol 2 pp 1ndash19 DeutschesGeoForschungsZentrum GFZ Potsdam Germany 2002

[23] V Vishal P G Ranjith and T N Singh ldquoAn experimentalinvestigation on behaviour of coal under fluid saturationusing acoustic emissionrdquo Journal of Natural Gas Science andEngineering vol 22 pp 428ndash436 2015

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

Shock and Vibration 9

CSA2CSB2CSC2CMSA2GSB1GSB3

CSB3CSB1

CMSA1CMSA3GSB2

Loading stage

100

80

60

40

20

0

MS

even

t acc

umul

atio

n (

)

I II III IV V VI VII VIII IX X

Figure 7 The event accumulation variation during the loadingstage

and the rule of crack or cleat initiation and propagation inthe samples Furthermore the intrinsic resonant frequenciesof variations of materials also contribute to the monitoringresult

52MS Event Count and Event Rate Asmentioned above weused the STALTA method to recognize effective MS eventcounting the ring and event number During the detection ofeffective MS signal if the ratio of STA and LTA is over thethreshold the number counter of MS event and ring will betriggered but the difference is that a single event will not stopuntil the value goes back to being under the threshold whilethe ring number counts all single section over the criticalamplitude It can be seen from the study that the MS eventand ring count are quite different for these three kinds ofmaterials and this shows irregular characteristics even inthe same kind of material (Tables 2 and 3) An interestingphenomenon is that cement specimen procedures least MSevent in comparison with coal and glass However as theloading time for each sample is quite different the eventrate value is quite similar for all the samples The averageevent rate per second is around 2 but for cement sample likeCMSA1 and CMSA3 it is only 025 and 022

Figure 7 shows the total event accumulation ratio of thewhole loading process The variations tendencies are quitedifferent for different sample for one group of the samples(CSB2 CSA2 CSB3 GSB1 and GSB3) shows linear type ofincrease in the loading process while for the other seriesof samples (CMSA1 GSB2 CMSA2 and CMSA3) eventaccumulation increases smoothly at first (stages IndashV) andgrows sharply after that (stages VIndashX) These two types oftendencies give an illustration of MS activities in the whole

Specimen number

dc1dc2dc3dc4

dc5dc6dc7dc8

CSA

2

CMSA

2

GSB

1

GSB

3

GSB

2

CSB3

CSB1

CMSA

1

CMSA

3

00

02

04

06

08

10

Ener

gy p

ropo

rtio

n (

)

Figure 8 Energy proportion of WPT best tree decomposition

process and this phenomenon has great agreement with thestress and material damage rules

53The Energy Distribution Characteristics of MS Signal Theanalysis on energy distribution ofMS signal is based on resultofWPT best tree transformation It can be seen from Figure 1that the signal is decomposed into 8 decompositions denotedas dc1 to dc8 According to the node number the corre-sponding frequency bands of each node component are 0ndash469Hz 469ndash938Hz 938ndash1406Hz 1406ndash1875Hz 1875ndash3750Hz 3750ndash750Hz 750ndash10781Hz and 10781ndash1500Hzrespectively Examples of several specimens in terms ofenergy distribution of the eight decompositions are shownin Figure 8 Hereby the energy distribution analysis alsoshows the frequency-spectrum characteristics of differentmaterials It is obvious to see and conclude that all specimensmade of different materials are abundant with low frequencycomponents especially for dc1 However some differencesbetween each material type could also be found In the casesof glass specimens the total signal energy for each specimenin 0ndash469Hz is all more than 50 (629 958 and 507)As glass is a kind of typical brittle material there are alwaysbig fractures generated directly when it is compressed anddeformed This kind of rapid release of energy will limitthe energy accumulation and the big fractures all limit thespreading of stress wave Consequently the signal energyis mainly from low frequency domain By contrast theenergy proportions for cement produced signals are widelydistributed in all frequency domains mainly covering the 0ndash938Hz and 1406ndash750Hz But for coal the situation is muchmore complicated The main energy distribution domain forevery sample seems to change greatly in their medium and

10 Shock and Vibration

Table 2 Ring count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X TotalCSA2 6367 7147 8068 5044 4966 4792 3178 4998 2793 11400 58753CSB1 194 1666 892 140 59 22 71 174 2399 4145 9762CSB2 1797 1938 1430 1264 1746 1117 814 1363 932 1570 13971CSB3 13174 6847 8510 8543 6849 7595 8019 10858 7705 9417 87517CSC2 1596 6451 3153 569 640 123 0 122 177 3259 16090CMSA1 630 57 43 0 905 567 238 686 707 1686 5519CMSA2 243 0 152 25 482 211 362 597 1146 2350 5568CMSA3 1652 325 199 175 667 1203 1563 3438 11684 11132 32038GSB1 6961 5387 7107 4334 3437 4303 3160 2680 3460 1507 42336GSB2 2016 2521 2578 2613 3802 6703 17618 23977 23964 21802 107594GSB3 11958 9097 10882 16121 11292 9896 10314 11962 18636 17162 127320

Table 3 MS event count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X Total Event rateCSA2 311 294 302 219 239 226 159 238 121 368 2477 236CSB1 23 57 27 4 2 1 3 6 84 118 325 217CSB2 94 111 93 78 101 71 61 81 67 77 834 309CSB3 559 409 428 472 359 405 413 519 388 478 4430 242CSC2 83 132 84 14 11 3 0 2 4 87 420 14CMSA1 10 0 6 2 4 6 17 21 36 81 183 022CMSA2 28 3 2 0 37 21 10 36 36 73 246 025CMSA3 66 34 20 21 27 67 66 108 374 333 1116 13GSB1 220 162 187 133 76 117 103 91 96 58 1243 143GSB2 100 92 107 90 176 268 531 609 602 603 3178 195GSB3 401 323 382 505 378 335 342 417 540 535 4158 258

high frequency components Specifically CSA2 has morefrequency components for d8 and CSB1 has more for d5while CSB3 is abundant with d7 This may be mainly becauseof the differences for loading rate and coal powder sizes andof course the boundaries and interfaces inside the samples

54 Relationship between MS Signal and Loading Rate Toanalyze the microseismic characteristics of different materi-als different loading rate is set in the process of experimentAs mentioned above coal cement paste and glass speci-mens have quite different mechanic strength and materialcharacteristics Thus we set different loading rate variationfor each type of materials for coal it is 5ndash20Ns and forcement and glass it is 150ndash300Ns Taking coal samples asan example the statistics on CSB1 CSB2 and CSB3 showthat the loading peak force values increase as the loading rateset larger and the bigger the loading rate the more the MSevents and ring number activities recodedThe figure for thisis 9762 and 13971 to 87517 in ring number and 325834 to 4430in MS single event Furthermore similar result for cementand glass sample can be found regarding the variation ofloading rate We believe that this is because MS phenomenonis actually kind of stress wave and release of energy and largerloading speed results in more rapid energy accumulating and

releasing outThus there aremoreMS events and ring countswere founded during the whole loading process

55 Relationship between MS Signal and Coal Powder ParticleSize A group of coal specimens (CSA2 CSB2 and CSC2)is selected to analyze the effect of coal powder size on MScharacteristics As shown in Table 1 under the same loadingrate (10Ns) the peak values are 774 kN 272 kN and 318 kNfor specimens made by particles size in 0ndash025mm 025ndash05mm and 05ndash2mm Theoretically the smaller the size ofpowders used is the larger the strength of pressured briquettesamples should be for small particles could bond wellAlthough CSB2 which was made up of 025ndash05mm particlesize powders only got 272 kN loading peak value before theprimary damage the general trend between CSA2 and CSC2showed an obvious decrease of sample strength with powdersize going up As bigger strength means more loading time itis not surprising thatmoreMS events were recorded for CSA2and CSC2 (Table 2) On the contrary CSB2 has the largestevent rate and it is 309 events every second as shown inTable 3 Another feature by which powder size will influencethe man-made specimen structure is the homogeneity Thisis because specimens made by larger particle size of powderhave more cracks inside As a result it will take longer time

Shock and Vibration 11

during the crack closure stage and finally get into linear elasticstage Also as shown in Figure 7 the curves of MS eventaccumulation with loading stage for CSA2 and CSB2 increasemore linearly while for CSC2 made by largest powder itis quite different In summary although the loading timespecimen strength and events statistic for CSB2 show someabnormality comparedwith general trend from small powdersize to large powder size made specimen the global eventaccumulation has great consistency with theoretical analysis

6 Conclusion

A total of 11 experiment tests on coal cement paste andglass specimens were carried out under uniaxial compressioncondition Coal powder particle size material type andloading rate are selected to compare and analyzeMSAE char-acteristic In the meantime the MS event rate accumulatedringing number relationship between loading stress and MSevent energy distribution and frequency-time characteris-tics of MS signal produced during the damage of differentspecimens were also discussed in the work

To separate the MS signal from the noises pollutedsignals the combination of wavelet packet transform (WPT)and local mean decomposition (LMD)methodwas chosen todenoise and analyze all the signals collected The applicationof this new method proved that the WPT-LMD method hada good performance in denoising and filtering the MS dataand it also could be used as common tool in the issues offrequency-spectrum characteristics analysis of MS signal orMS-like nonstationary and nonlinear signal

The investigation result on theMS signal features of threecategories of materials showed the following

(1) In terms of the damage and deformation of coal andcement in the early stage few effective signals areproduced then specimens will experience a relativeclam middle stage and in the poststage where mainrupture occurred a large number of MS events wererecorded But for glass samples theMS event genera-tions were throughout the whole loading process Weconclude that this is because of the brittle and crispyfeatures of glass material which means less energystored and quickly released

(2) Also there is good corresponding relation betweenloading time and MS events and ring numbers thelonger loading time is the more MS events and ringnumbers are detected while for loading rate there isno obvious effect on MS

(3) In the case of relationship for coal powder par-ticle size and MS feature we found that the MSaccumulation patterns of small particles (0ndash05mm)made specimens be more linear while large particles(05ndash2mm) made specimens own typical nonlinearcharacteristics The latter will spend much more timeto close the crack and be crushed into small powdersThere seems to be a decrease trend of specimenstrengths with the powder size used from small tolarge but there is abnormal case of CSB2 Similar

conclusion could be made for event and ring numbercounts and event rate this may be caused by a failureof specimen preparation and this is supposed to beexamined in the further study

(4) From the frequency-spectrum point of view all testson differentmaterials showedwide frequency domaincharacteristics which are fallen in the domain of0ndash200Hz 300ndash500Hz and 1000ndash1500Hz Howeverit should be noted that the frequency domain ofglass specimens is mainly concentrated in the lowfrequency (0ndash938Hz) due to their extremely brittlemechanical characteristics

To sum up we believe that the outcome of the paperwill be of great significance to improve the dynamic disastersforecast and improve coal mining safety This may embodysome promotions in data processing by using theWPT-LMDmethod and provide a theoretical basis for understandingthe mechanism of MS activities due to the experimentalphenomenon and conclusions presented

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This study was supported by National Natural Science Foun-dation of China (Grant no 51274206) The correspondingauthor also acknowledged the financial support provided byChinese Scholarship Council (CSC)

References

[1] C U Grosse andM Ohtsu Acoustic Emission Testing SpringerBerlin Germany 2008

[2] H RHardy JrAcoustic EmissionMicroseismic Activity Volume1 Principles Techniques and Geotechnical Applications CRCPress 2005

[3] C-P Lu L-M Dou B Liu Y-S Xie and H-S Liu ldquoMicroseis-mic low-frequency precursor effect of bursting failure of coaland rockrdquo Journal of Applied Geophysics vol 79 pp 55ndash63 2012

[4] C-P Lu L-M Dou H Liu H-S Liu B Liu and B-B DuldquoCase study on microseismic effect of coal and gas outburstprocessrdquo International Journal of Rock Mechanics and MiningSciences vol 53 pp 101ndash110 2012

[5] S I Qin J Cheng and S Zhu ldquoThe application and prospect ofmicroseismic technique in coalminerdquo Procedia EnvironmentalSciences vol 12 pp 218ndash224 2012

[6] X H Liu C X Liu S Zhuang L Wang and S S LinldquoLaboratory studies on acoustic emission characteristics to coaldynamic response under variable accelerative loadrdquo AppliedMechanics and Materials vol 580ndash583 pp 623ndash627 2014

[7] N Cook ldquoThe seismic location of rockburstsrdquo in Proceedings ofthe 5th Rock Mechanics Symposium pp 463ndash516 1963

[8] A G Beattie ldquoAcoustic emission principles and instrumenta-tionrdquo Journal of Acoustic Emission vol 2 no 12 pp 95ndash1281983

12 Shock and Vibration

[9] L Obert ldquoThe microseismic method discovery and earlyhistoryrdquo in Proceedings of the 1st Conference on Acoustic Emis-sionMicroseismic Activity in Geologic Structures and Materialspp 11ndash12 1977

[10] I Yamada K Masuda and H Mizutani ldquoElectromagnetic andacoustic emission associated with rock fracturerdquo Physics of theEarth and Planetary Interiors vol 57 no 1-2 pp 157ndash168 1989

[11] C-P Lu L-M Dou N Zhang et al ldquoMicroseismic frequency-spectrum evolutionary rule of rockburst triggered by roof fallrdquoInternational Journal of Rock Mechanics and Mining Sciencesvol 64 pp 6ndash16 2013

[12] S Sasaki ldquoCharacteristics of microseismic events inducedduring hydraulic fracturing experiments at the Hijiori hot dryrock geothermal energy site Yamagata Japanrdquo Tectonophysicsvol 289 no 1ndash3 pp 171ndash188 1998

[13] F Spaepen ldquoAmicroscopicmechanism for steady state inhomo-geneous flow in metallic glassesrdquo Acta Metallurgica vol 25 no4 pp 407ndash415 1977

[14] P E Tikkanen ldquoNonlinear wavelet and wavelet packet denois-ing of electrocardiogram signalrdquo Biological Cybernetics vol 80no 4 pp 259ndash267 1999

[15] O A M Aly A S Omar and A Z Elsherbeni ldquoDetectionand localization of RF radar pulses in noise environments usingwavelet packet transform and higher order statisticsrdquo Progressin Electromagnetics Research vol 58 pp 301ndash317 2006

[16] A Kyprianou P L Lewin V Efthimiou A Stavrou and GE Georghiou ldquoWavelet packet denoising for online partialdischarge detection in cables and its application to experimentalfield resultsrdquoMeasurement Science and Technology vol 17 no 9pp 2367ndash2379 2006

[17] J S Smith ldquoThe local mean decomposition and its applicationto EEG perception datardquo Journal of the Royal Society Interfacevol 2 no 5 pp 443ndash454 2005

[18] Z Sun and C C Chang ldquoStructural damage assessment basedon wavelet packet transformrdquo Journal of Structural Engineeringvol 128 no 10 pp 1354ndash1361 2002

[19] J-D Wu and C-H Liu ldquoAn expert system for fault diagnosisin internal combustion engines using wavelet packet transformand neural networkrdquo Expert Systems with Applications vol 36no 3 pp 4278ndash4286 2009

[20] Y Wang Z He and Y Zi ldquoA demodulation method based onimproved local mean decomposition and its application in rub-impact fault diagnosisrdquo Measurement Science and Technologyvol 20 no 2 Article ID 025704 2009

[21] Y Wang Z He and Y Zi ldquoA comparative study on thelocal mean decomposition and empirical mode decompositionand their applications to rotating machinery health diagnosisrdquoJournal of Vibration and Acoustics vol 132 no 2 Article ID021010 2010

[22] A Trnkoczy ldquoUnderstanding and parameter setting ofSTALTA trigger algorithmrdquo in IASPEI New Manual ofSeismological Observatory Practice vol 2 pp 1ndash19 DeutschesGeoForschungsZentrum GFZ Potsdam Germany 2002

[23] V Vishal P G Ranjith and T N Singh ldquoAn experimentalinvestigation on behaviour of coal under fluid saturationusing acoustic emissionrdquo Journal of Natural Gas Science andEngineering vol 22 pp 428ndash436 2015

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

10 Shock and Vibration

Table 2 Ring count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X TotalCSA2 6367 7147 8068 5044 4966 4792 3178 4998 2793 11400 58753CSB1 194 1666 892 140 59 22 71 174 2399 4145 9762CSB2 1797 1938 1430 1264 1746 1117 814 1363 932 1570 13971CSB3 13174 6847 8510 8543 6849 7595 8019 10858 7705 9417 87517CSC2 1596 6451 3153 569 640 123 0 122 177 3259 16090CMSA1 630 57 43 0 905 567 238 686 707 1686 5519CMSA2 243 0 152 25 482 211 362 597 1146 2350 5568CMSA3 1652 325 199 175 667 1203 1563 3438 11684 11132 32038GSB1 6961 5387 7107 4334 3437 4303 3160 2680 3460 1507 42336GSB2 2016 2521 2578 2613 3802 6703 17618 23977 23964 21802 107594GSB3 11958 9097 10882 16121 11292 9896 10314 11962 18636 17162 127320

Table 3 MS event count distribution and accumulation in different loading stage

Sample I II III IV V VI VII VIII IX X Total Event rateCSA2 311 294 302 219 239 226 159 238 121 368 2477 236CSB1 23 57 27 4 2 1 3 6 84 118 325 217CSB2 94 111 93 78 101 71 61 81 67 77 834 309CSB3 559 409 428 472 359 405 413 519 388 478 4430 242CSC2 83 132 84 14 11 3 0 2 4 87 420 14CMSA1 10 0 6 2 4 6 17 21 36 81 183 022CMSA2 28 3 2 0 37 21 10 36 36 73 246 025CMSA3 66 34 20 21 27 67 66 108 374 333 1116 13GSB1 220 162 187 133 76 117 103 91 96 58 1243 143GSB2 100 92 107 90 176 268 531 609 602 603 3178 195GSB3 401 323 382 505 378 335 342 417 540 535 4158 258

high frequency components Specifically CSA2 has morefrequency components for d8 and CSB1 has more for d5while CSB3 is abundant with d7 This may be mainly becauseof the differences for loading rate and coal powder sizes andof course the boundaries and interfaces inside the samples

54 Relationship between MS Signal and Loading Rate Toanalyze the microseismic characteristics of different materi-als different loading rate is set in the process of experimentAs mentioned above coal cement paste and glass speci-mens have quite different mechanic strength and materialcharacteristics Thus we set different loading rate variationfor each type of materials for coal it is 5ndash20Ns and forcement and glass it is 150ndash300Ns Taking coal samples asan example the statistics on CSB1 CSB2 and CSB3 showthat the loading peak force values increase as the loading rateset larger and the bigger the loading rate the more the MSevents and ring number activities recodedThe figure for thisis 9762 and 13971 to 87517 in ring number and 325834 to 4430in MS single event Furthermore similar result for cementand glass sample can be found regarding the variation ofloading rate We believe that this is because MS phenomenonis actually kind of stress wave and release of energy and largerloading speed results in more rapid energy accumulating and

releasing outThus there aremoreMS events and ring countswere founded during the whole loading process

55 Relationship between MS Signal and Coal Powder ParticleSize A group of coal specimens (CSA2 CSB2 and CSC2)is selected to analyze the effect of coal powder size on MScharacteristics As shown in Table 1 under the same loadingrate (10Ns) the peak values are 774 kN 272 kN and 318 kNfor specimens made by particles size in 0ndash025mm 025ndash05mm and 05ndash2mm Theoretically the smaller the size ofpowders used is the larger the strength of pressured briquettesamples should be for small particles could bond wellAlthough CSB2 which was made up of 025ndash05mm particlesize powders only got 272 kN loading peak value before theprimary damage the general trend between CSA2 and CSC2showed an obvious decrease of sample strength with powdersize going up As bigger strength means more loading time itis not surprising thatmoreMS events were recorded for CSA2and CSC2 (Table 2) On the contrary CSB2 has the largestevent rate and it is 309 events every second as shown inTable 3 Another feature by which powder size will influencethe man-made specimen structure is the homogeneity Thisis because specimens made by larger particle size of powderhave more cracks inside As a result it will take longer time

Shock and Vibration 11

during the crack closure stage and finally get into linear elasticstage Also as shown in Figure 7 the curves of MS eventaccumulation with loading stage for CSA2 and CSB2 increasemore linearly while for CSC2 made by largest powder itis quite different In summary although the loading timespecimen strength and events statistic for CSB2 show someabnormality comparedwith general trend from small powdersize to large powder size made specimen the global eventaccumulation has great consistency with theoretical analysis

6 Conclusion

A total of 11 experiment tests on coal cement paste andglass specimens were carried out under uniaxial compressioncondition Coal powder particle size material type andloading rate are selected to compare and analyzeMSAE char-acteristic In the meantime the MS event rate accumulatedringing number relationship between loading stress and MSevent energy distribution and frequency-time characteris-tics of MS signal produced during the damage of differentspecimens were also discussed in the work

To separate the MS signal from the noises pollutedsignals the combination of wavelet packet transform (WPT)and local mean decomposition (LMD)methodwas chosen todenoise and analyze all the signals collected The applicationof this new method proved that the WPT-LMD method hada good performance in denoising and filtering the MS dataand it also could be used as common tool in the issues offrequency-spectrum characteristics analysis of MS signal orMS-like nonstationary and nonlinear signal

The investigation result on theMS signal features of threecategories of materials showed the following

(1) In terms of the damage and deformation of coal andcement in the early stage few effective signals areproduced then specimens will experience a relativeclam middle stage and in the poststage where mainrupture occurred a large number of MS events wererecorded But for glass samples theMS event genera-tions were throughout the whole loading process Weconclude that this is because of the brittle and crispyfeatures of glass material which means less energystored and quickly released

(2) Also there is good corresponding relation betweenloading time and MS events and ring numbers thelonger loading time is the more MS events and ringnumbers are detected while for loading rate there isno obvious effect on MS

(3) In the case of relationship for coal powder par-ticle size and MS feature we found that the MSaccumulation patterns of small particles (0ndash05mm)made specimens be more linear while large particles(05ndash2mm) made specimens own typical nonlinearcharacteristics The latter will spend much more timeto close the crack and be crushed into small powdersThere seems to be a decrease trend of specimenstrengths with the powder size used from small tolarge but there is abnormal case of CSB2 Similar

conclusion could be made for event and ring numbercounts and event rate this may be caused by a failureof specimen preparation and this is supposed to beexamined in the further study

(4) From the frequency-spectrum point of view all testson differentmaterials showedwide frequency domaincharacteristics which are fallen in the domain of0ndash200Hz 300ndash500Hz and 1000ndash1500Hz Howeverit should be noted that the frequency domain ofglass specimens is mainly concentrated in the lowfrequency (0ndash938Hz) due to their extremely brittlemechanical characteristics

To sum up we believe that the outcome of the paperwill be of great significance to improve the dynamic disastersforecast and improve coal mining safety This may embodysome promotions in data processing by using theWPT-LMDmethod and provide a theoretical basis for understandingthe mechanism of MS activities due to the experimentalphenomenon and conclusions presented

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This study was supported by National Natural Science Foun-dation of China (Grant no 51274206) The correspondingauthor also acknowledged the financial support provided byChinese Scholarship Council (CSC)

References

[1] C U Grosse andM Ohtsu Acoustic Emission Testing SpringerBerlin Germany 2008

[2] H RHardy JrAcoustic EmissionMicroseismic Activity Volume1 Principles Techniques and Geotechnical Applications CRCPress 2005

[3] C-P Lu L-M Dou B Liu Y-S Xie and H-S Liu ldquoMicroseis-mic low-frequency precursor effect of bursting failure of coaland rockrdquo Journal of Applied Geophysics vol 79 pp 55ndash63 2012

[4] C-P Lu L-M Dou H Liu H-S Liu B Liu and B-B DuldquoCase study on microseismic effect of coal and gas outburstprocessrdquo International Journal of Rock Mechanics and MiningSciences vol 53 pp 101ndash110 2012

[5] S I Qin J Cheng and S Zhu ldquoThe application and prospect ofmicroseismic technique in coalminerdquo Procedia EnvironmentalSciences vol 12 pp 218ndash224 2012

[6] X H Liu C X Liu S Zhuang L Wang and S S LinldquoLaboratory studies on acoustic emission characteristics to coaldynamic response under variable accelerative loadrdquo AppliedMechanics and Materials vol 580ndash583 pp 623ndash627 2014

[7] N Cook ldquoThe seismic location of rockburstsrdquo in Proceedings ofthe 5th Rock Mechanics Symposium pp 463ndash516 1963

[8] A G Beattie ldquoAcoustic emission principles and instrumenta-tionrdquo Journal of Acoustic Emission vol 2 no 12 pp 95ndash1281983

12 Shock and Vibration

[9] L Obert ldquoThe microseismic method discovery and earlyhistoryrdquo in Proceedings of the 1st Conference on Acoustic Emis-sionMicroseismic Activity in Geologic Structures and Materialspp 11ndash12 1977

[10] I Yamada K Masuda and H Mizutani ldquoElectromagnetic andacoustic emission associated with rock fracturerdquo Physics of theEarth and Planetary Interiors vol 57 no 1-2 pp 157ndash168 1989

[11] C-P Lu L-M Dou N Zhang et al ldquoMicroseismic frequency-spectrum evolutionary rule of rockburst triggered by roof fallrdquoInternational Journal of Rock Mechanics and Mining Sciencesvol 64 pp 6ndash16 2013

[12] S Sasaki ldquoCharacteristics of microseismic events inducedduring hydraulic fracturing experiments at the Hijiori hot dryrock geothermal energy site Yamagata Japanrdquo Tectonophysicsvol 289 no 1ndash3 pp 171ndash188 1998

[13] F Spaepen ldquoAmicroscopicmechanism for steady state inhomo-geneous flow in metallic glassesrdquo Acta Metallurgica vol 25 no4 pp 407ndash415 1977

[14] P E Tikkanen ldquoNonlinear wavelet and wavelet packet denois-ing of electrocardiogram signalrdquo Biological Cybernetics vol 80no 4 pp 259ndash267 1999

[15] O A M Aly A S Omar and A Z Elsherbeni ldquoDetectionand localization of RF radar pulses in noise environments usingwavelet packet transform and higher order statisticsrdquo Progressin Electromagnetics Research vol 58 pp 301ndash317 2006

[16] A Kyprianou P L Lewin V Efthimiou A Stavrou and GE Georghiou ldquoWavelet packet denoising for online partialdischarge detection in cables and its application to experimentalfield resultsrdquoMeasurement Science and Technology vol 17 no 9pp 2367ndash2379 2006

[17] J S Smith ldquoThe local mean decomposition and its applicationto EEG perception datardquo Journal of the Royal Society Interfacevol 2 no 5 pp 443ndash454 2005

[18] Z Sun and C C Chang ldquoStructural damage assessment basedon wavelet packet transformrdquo Journal of Structural Engineeringvol 128 no 10 pp 1354ndash1361 2002

[19] J-D Wu and C-H Liu ldquoAn expert system for fault diagnosisin internal combustion engines using wavelet packet transformand neural networkrdquo Expert Systems with Applications vol 36no 3 pp 4278ndash4286 2009

[20] Y Wang Z He and Y Zi ldquoA demodulation method based onimproved local mean decomposition and its application in rub-impact fault diagnosisrdquo Measurement Science and Technologyvol 20 no 2 Article ID 025704 2009

[21] Y Wang Z He and Y Zi ldquoA comparative study on thelocal mean decomposition and empirical mode decompositionand their applications to rotating machinery health diagnosisrdquoJournal of Vibration and Acoustics vol 132 no 2 Article ID021010 2010

[22] A Trnkoczy ldquoUnderstanding and parameter setting ofSTALTA trigger algorithmrdquo in IASPEI New Manual ofSeismological Observatory Practice vol 2 pp 1ndash19 DeutschesGeoForschungsZentrum GFZ Potsdam Germany 2002

[23] V Vishal P G Ranjith and T N Singh ldquoAn experimentalinvestigation on behaviour of coal under fluid saturationusing acoustic emissionrdquo Journal of Natural Gas Science andEngineering vol 22 pp 428ndash436 2015

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

Shock and Vibration 11

during the crack closure stage and finally get into linear elasticstage Also as shown in Figure 7 the curves of MS eventaccumulation with loading stage for CSA2 and CSB2 increasemore linearly while for CSC2 made by largest powder itis quite different In summary although the loading timespecimen strength and events statistic for CSB2 show someabnormality comparedwith general trend from small powdersize to large powder size made specimen the global eventaccumulation has great consistency with theoretical analysis

6 Conclusion

A total of 11 experiment tests on coal cement paste andglass specimens were carried out under uniaxial compressioncondition Coal powder particle size material type andloading rate are selected to compare and analyzeMSAE char-acteristic In the meantime the MS event rate accumulatedringing number relationship between loading stress and MSevent energy distribution and frequency-time characteris-tics of MS signal produced during the damage of differentspecimens were also discussed in the work

To separate the MS signal from the noises pollutedsignals the combination of wavelet packet transform (WPT)and local mean decomposition (LMD)methodwas chosen todenoise and analyze all the signals collected The applicationof this new method proved that the WPT-LMD method hada good performance in denoising and filtering the MS dataand it also could be used as common tool in the issues offrequency-spectrum characteristics analysis of MS signal orMS-like nonstationary and nonlinear signal

The investigation result on theMS signal features of threecategories of materials showed the following

(1) In terms of the damage and deformation of coal andcement in the early stage few effective signals areproduced then specimens will experience a relativeclam middle stage and in the poststage where mainrupture occurred a large number of MS events wererecorded But for glass samples theMS event genera-tions were throughout the whole loading process Weconclude that this is because of the brittle and crispyfeatures of glass material which means less energystored and quickly released

(2) Also there is good corresponding relation betweenloading time and MS events and ring numbers thelonger loading time is the more MS events and ringnumbers are detected while for loading rate there isno obvious effect on MS

(3) In the case of relationship for coal powder par-ticle size and MS feature we found that the MSaccumulation patterns of small particles (0ndash05mm)made specimens be more linear while large particles(05ndash2mm) made specimens own typical nonlinearcharacteristics The latter will spend much more timeto close the crack and be crushed into small powdersThere seems to be a decrease trend of specimenstrengths with the powder size used from small tolarge but there is abnormal case of CSB2 Similar

conclusion could be made for event and ring numbercounts and event rate this may be caused by a failureof specimen preparation and this is supposed to beexamined in the further study

(4) From the frequency-spectrum point of view all testson differentmaterials showedwide frequency domaincharacteristics which are fallen in the domain of0ndash200Hz 300ndash500Hz and 1000ndash1500Hz Howeverit should be noted that the frequency domain ofglass specimens is mainly concentrated in the lowfrequency (0ndash938Hz) due to their extremely brittlemechanical characteristics

To sum up we believe that the outcome of the paperwill be of great significance to improve the dynamic disastersforecast and improve coal mining safety This may embodysome promotions in data processing by using theWPT-LMDmethod and provide a theoretical basis for understandingthe mechanism of MS activities due to the experimentalphenomenon and conclusions presented

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This study was supported by National Natural Science Foun-dation of China (Grant no 51274206) The correspondingauthor also acknowledged the financial support provided byChinese Scholarship Council (CSC)

References

[1] C U Grosse andM Ohtsu Acoustic Emission Testing SpringerBerlin Germany 2008

[2] H RHardy JrAcoustic EmissionMicroseismic Activity Volume1 Principles Techniques and Geotechnical Applications CRCPress 2005

[3] C-P Lu L-M Dou B Liu Y-S Xie and H-S Liu ldquoMicroseis-mic low-frequency precursor effect of bursting failure of coaland rockrdquo Journal of Applied Geophysics vol 79 pp 55ndash63 2012

[4] C-P Lu L-M Dou H Liu H-S Liu B Liu and B-B DuldquoCase study on microseismic effect of coal and gas outburstprocessrdquo International Journal of Rock Mechanics and MiningSciences vol 53 pp 101ndash110 2012

[5] S I Qin J Cheng and S Zhu ldquoThe application and prospect ofmicroseismic technique in coalminerdquo Procedia EnvironmentalSciences vol 12 pp 218ndash224 2012

[6] X H Liu C X Liu S Zhuang L Wang and S S LinldquoLaboratory studies on acoustic emission characteristics to coaldynamic response under variable accelerative loadrdquo AppliedMechanics and Materials vol 580ndash583 pp 623ndash627 2014

[7] N Cook ldquoThe seismic location of rockburstsrdquo in Proceedings ofthe 5th Rock Mechanics Symposium pp 463ndash516 1963

[8] A G Beattie ldquoAcoustic emission principles and instrumenta-tionrdquo Journal of Acoustic Emission vol 2 no 12 pp 95ndash1281983

12 Shock and Vibration

[9] L Obert ldquoThe microseismic method discovery and earlyhistoryrdquo in Proceedings of the 1st Conference on Acoustic Emis-sionMicroseismic Activity in Geologic Structures and Materialspp 11ndash12 1977

[10] I Yamada K Masuda and H Mizutani ldquoElectromagnetic andacoustic emission associated with rock fracturerdquo Physics of theEarth and Planetary Interiors vol 57 no 1-2 pp 157ndash168 1989

[11] C-P Lu L-M Dou N Zhang et al ldquoMicroseismic frequency-spectrum evolutionary rule of rockburst triggered by roof fallrdquoInternational Journal of Rock Mechanics and Mining Sciencesvol 64 pp 6ndash16 2013

[12] S Sasaki ldquoCharacteristics of microseismic events inducedduring hydraulic fracturing experiments at the Hijiori hot dryrock geothermal energy site Yamagata Japanrdquo Tectonophysicsvol 289 no 1ndash3 pp 171ndash188 1998

[13] F Spaepen ldquoAmicroscopicmechanism for steady state inhomo-geneous flow in metallic glassesrdquo Acta Metallurgica vol 25 no4 pp 407ndash415 1977

[14] P E Tikkanen ldquoNonlinear wavelet and wavelet packet denois-ing of electrocardiogram signalrdquo Biological Cybernetics vol 80no 4 pp 259ndash267 1999

[15] O A M Aly A S Omar and A Z Elsherbeni ldquoDetectionand localization of RF radar pulses in noise environments usingwavelet packet transform and higher order statisticsrdquo Progressin Electromagnetics Research vol 58 pp 301ndash317 2006

[16] A Kyprianou P L Lewin V Efthimiou A Stavrou and GE Georghiou ldquoWavelet packet denoising for online partialdischarge detection in cables and its application to experimentalfield resultsrdquoMeasurement Science and Technology vol 17 no 9pp 2367ndash2379 2006

[17] J S Smith ldquoThe local mean decomposition and its applicationto EEG perception datardquo Journal of the Royal Society Interfacevol 2 no 5 pp 443ndash454 2005

[18] Z Sun and C C Chang ldquoStructural damage assessment basedon wavelet packet transformrdquo Journal of Structural Engineeringvol 128 no 10 pp 1354ndash1361 2002

[19] J-D Wu and C-H Liu ldquoAn expert system for fault diagnosisin internal combustion engines using wavelet packet transformand neural networkrdquo Expert Systems with Applications vol 36no 3 pp 4278ndash4286 2009

[20] Y Wang Z He and Y Zi ldquoA demodulation method based onimproved local mean decomposition and its application in rub-impact fault diagnosisrdquo Measurement Science and Technologyvol 20 no 2 Article ID 025704 2009

[21] Y Wang Z He and Y Zi ldquoA comparative study on thelocal mean decomposition and empirical mode decompositionand their applications to rotating machinery health diagnosisrdquoJournal of Vibration and Acoustics vol 132 no 2 Article ID021010 2010

[22] A Trnkoczy ldquoUnderstanding and parameter setting ofSTALTA trigger algorithmrdquo in IASPEI New Manual ofSeismological Observatory Practice vol 2 pp 1ndash19 DeutschesGeoForschungsZentrum GFZ Potsdam Germany 2002

[23] V Vishal P G Ranjith and T N Singh ldquoAn experimentalinvestigation on behaviour of coal under fluid saturationusing acoustic emissionrdquo Journal of Natural Gas Science andEngineering vol 22 pp 428ndash436 2015

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

12 Shock and Vibration

[9] L Obert ldquoThe microseismic method discovery and earlyhistoryrdquo in Proceedings of the 1st Conference on Acoustic Emis-sionMicroseismic Activity in Geologic Structures and Materialspp 11ndash12 1977

[10] I Yamada K Masuda and H Mizutani ldquoElectromagnetic andacoustic emission associated with rock fracturerdquo Physics of theEarth and Planetary Interiors vol 57 no 1-2 pp 157ndash168 1989

[11] C-P Lu L-M Dou N Zhang et al ldquoMicroseismic frequency-spectrum evolutionary rule of rockburst triggered by roof fallrdquoInternational Journal of Rock Mechanics and Mining Sciencesvol 64 pp 6ndash16 2013

[12] S Sasaki ldquoCharacteristics of microseismic events inducedduring hydraulic fracturing experiments at the Hijiori hot dryrock geothermal energy site Yamagata Japanrdquo Tectonophysicsvol 289 no 1ndash3 pp 171ndash188 1998

[13] F Spaepen ldquoAmicroscopicmechanism for steady state inhomo-geneous flow in metallic glassesrdquo Acta Metallurgica vol 25 no4 pp 407ndash415 1977

[14] P E Tikkanen ldquoNonlinear wavelet and wavelet packet denois-ing of electrocardiogram signalrdquo Biological Cybernetics vol 80no 4 pp 259ndash267 1999

[15] O A M Aly A S Omar and A Z Elsherbeni ldquoDetectionand localization of RF radar pulses in noise environments usingwavelet packet transform and higher order statisticsrdquo Progressin Electromagnetics Research vol 58 pp 301ndash317 2006

[16] A Kyprianou P L Lewin V Efthimiou A Stavrou and GE Georghiou ldquoWavelet packet denoising for online partialdischarge detection in cables and its application to experimentalfield resultsrdquoMeasurement Science and Technology vol 17 no 9pp 2367ndash2379 2006

[17] J S Smith ldquoThe local mean decomposition and its applicationto EEG perception datardquo Journal of the Royal Society Interfacevol 2 no 5 pp 443ndash454 2005

[18] Z Sun and C C Chang ldquoStructural damage assessment basedon wavelet packet transformrdquo Journal of Structural Engineeringvol 128 no 10 pp 1354ndash1361 2002

[19] J-D Wu and C-H Liu ldquoAn expert system for fault diagnosisin internal combustion engines using wavelet packet transformand neural networkrdquo Expert Systems with Applications vol 36no 3 pp 4278ndash4286 2009

[20] Y Wang Z He and Y Zi ldquoA demodulation method based onimproved local mean decomposition and its application in rub-impact fault diagnosisrdquo Measurement Science and Technologyvol 20 no 2 Article ID 025704 2009

[21] Y Wang Z He and Y Zi ldquoA comparative study on thelocal mean decomposition and empirical mode decompositionand their applications to rotating machinery health diagnosisrdquoJournal of Vibration and Acoustics vol 132 no 2 Article ID021010 2010

[22] A Trnkoczy ldquoUnderstanding and parameter setting ofSTALTA trigger algorithmrdquo in IASPEI New Manual ofSeismological Observatory Practice vol 2 pp 1ndash19 DeutschesGeoForschungsZentrum GFZ Potsdam Germany 2002

[23] V Vishal P G Ranjith and T N Singh ldquoAn experimentalinvestigation on behaviour of coal under fluid saturationusing acoustic emissionrdquo Journal of Natural Gas Science andEngineering vol 22 pp 428ndash436 2015

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of