[mechanisms and machine science] vibration engineering and technology of machinery volume 23 ||...

Vibration-Based Condition Monitoringfor Rotating Machinery with DifferentFlexible Supports

Adrian D. Nembhard and Jyoti K. Sinha

Abstract In a previous study, a combined vibration and temperature analysistechnique for the diagnosis of commonly encountered rotor related faults producedgood practicable results even without the use of temperature. This was, however,developed on an experimental rig with relatively rigid supports. The objective of thecurrent study is to investigate the transferability of the said approach on rotatingmachines with relatively flexible supports. A fault diagnosis (FD) method whichcompares vibration data acquired from similar machines with different flexiblesupports is also considered here. The experimental rig used in the previous studywas modified to accommodate two different relatively flexible supports. The resultsof the previously proposed technique showed that diagnosis is insensitive to thesupport types used. The result of the newly introduced combined FD approach gaveimproved diagnosis over the previously proposed technique. The observationsmade are presented in this paper.

Keywords Vibration monitoring � Condition monitoring � Principal componentanalysis � Rotating machinery � Fault diagnosis

1 Introduction

In a previous study, Nembhard et al. [8, 9] applied a combined vibration andtemperature analysis technique for the diagnosis of commonly encountered rotor(also referred to as shaft) related faults on a small experimental rig with relativelyrigid supports. The study had several objectives, key among which were; to developa simple but robust diagnostic method that can use data from a single vibrationsensor and a single temperature sensor at each bearing while preserving moderatecomputational load for the diagnosis of crack misalignment and rub faults.

A.D. Nembhard (&) � J.K. SinhaSchool of Mechanical, Aerospace and Civil Engineering (MACE),The University of Manchester, Manchester M13 9PL, UKe-mail: [email protected]

© Springer International Publishing Switzerland 2015J.K. Sinha (ed.), Vibration Engineering and Technology of Machinery,Mechanisms and Machine Science 23, DOI 10.1007/978-3-319-09918-7_9

119

Condition indicators computed from the measured vibration data were processedwith and without bearing temperature measurements. It was concluded that theproposed technique produced an easily interpreted graphical representation whichhad good separation of the healthy condition from the faults tested both with andwithout the use of temperature.

The objective of the current study is to investigate the transferability of theapproach proposed by Nembhard et al. [8, 9] on two rotating machines with rela-tively flexible supports (FS1 and FS2). It is possible that the same rotating machinewhen installed at different plant sites exhibit different flexibility with differentnatural frequencies. However, the vibration data history requisite for conditionmonitoring and subsequent fault diagnosis may exist at one site and be lacking inthe other. A combined analysis for directly comparing the two different flexiblesupports tested (FS1 and FS2) was carried out to explore whether the method can beused for diagnosis of machines where such a scenario exists.

Thus, the experimental rig used by Nembhard et al. [8, 9] was modified toaccommodate two different relatively flexible bearing supports. Several samples ofon-bearing vibration data were collected at a steady state speed for a residualmisalignment with residual unbalance (RMRU) baseline condition as well as forshaft crack, coupling misalignment and shaft rub fault conditions. Testing was donewith one condition existing at a time, first with the rig set up for FS1 and afterwardsfor FS2. Widely applied health indicators (referred to as features) that best describethe state of the machine were computed from the measured vibration data. Threesets of data processing were done: previous technique on FS1 features, previoustechnique on FS2 features and then the newly introduced technique on FS1 and FS2features combined in a single analysis step. The results of the previous technique forboth supports gave good separation between the different conditions tested, thussuggesting the method is transferable to machines with relatively flexible of sup-ports and hence independent of machine supports. The results from newly proposedcombined technique showed improved clustering and separation between theconditions tested. The results obtained for the newly proposed technique suggestedthe method can be useful in practice for the comparison of vibration data acquiredfrom similarly configured rotating machines with different natural frequencies. Theobservations made are presented in this paper.

2 Experimental Set Up and Experiments Conducted

Figure 1 shows a schematic of the experimental rig and instrumentation used in thecurrent study. The rig is a fundamental model of a multi-stage rotating machine thatwas modified from the previous study done by Nembhard et al. [8, 9] to accom-modate relatively flexible supports. The rig’s rotating assembly consists of two20 mm diameter rigidly coupled (C2) dissimilar length bright mild steel shafts of1,000 and 500 mm length. The long shaft is connected to a three phase 0.75 kW2800 rpm electric motor by a semi-flexible coupling (C1). The rotating assembly is

120 A.D. Nembhard and J.K. Sinha

supported by four grease lubricated 20 mm internal diameter flanged ball bearings(B1–B4). As shown in Fig. 2a, b, each bearing is mounted by four flexible rods thatare secured to a flexible bearing pedestal. Each bearing pedestal is bolted to a largelathe bed that serves as the rig’s foundation. Machine sections which act as bal-ancing disks (125 mm in diameter and 14 mm thick) are mounted to each rotor; twodisks (D1–D2) for the long shaft and one disk (D1) for the short shaft.

The dynamic response of the rig was measured by four ICP type 100 mV/gaccelerometers; one each mounted at a 450 position on the flange of each bearing(see Fig. 2b). This mounting position was adopted to simultaneously account for themeasurement of both the vertical and horizontal responses from the sole acceler-ometer used at each bearing. The accelerometers were mounted on studs that wereattached to the bearings with super glue. The vibration data are transmitted from theaccelerometers to a four channel signal conditioner with a 1:1 gain and afterwardsto a National Instrument 16 bit analogue to digital (A/D) data acquisition (DAQ)system which is connected to a personal computer via a USB cable. The vibrationdata are then recorded and stored on a personal computer for later processing andanalysis.

Two different flexible supports were investigated. The support types weremodified by changing the diameter of the bearing mounting rods. Therefore, thefirst support tested, Flexible Support 1 (FS1) and second support tested, FlexibleSupport (FS2) consists of 10 mm and 6 mm diameter bearing mounting rodsrespectively. The impact-response method of modal testing [4] was conducted toconfirm that FS1 and FS2 were dynamically different from each other and also fromthe supports used in Nembhard et al. [8, 9]. The natural frequencies of the previousrig used by Nembhard et al. [8, 9] were identified by Elbhbah and Sinha [3] at 68,144 and 352.5 Hz. By appearance, the first four natural frequencies for FS1 are50.66, 56.76, 59.2 and 127 Hz. Similarly, the first four natural frequencies for FS2are 47, 55.54, 57.98 and 127 Hz.

In order to establish baseline operating conditions for the experimental rig, datawas first collected for a baseline condition. In practice, it is best for the baselinecondition to be as healthy as possible to be used as a reference [1] however, therewere some difficulty in getting the rig sufficiently aligned to be deemed fullyhealthy. As demonstrated in Fig. 3a, b, the baseline condition was characterised bysome residual misalignment with residual unbalance, and thus referred in thepresent study as the Residual Misalignment with Residual Unbalance (RMRU)case. With one fault condition existing at a time, testing was done for three rotorfault conditions; a transverse shaft crack, coupling misalignment and full shaft rub.Shaft crack was simulated by introducing a 0.33 mm wide by 4 mm (20 % of shaftdiameter) deep transverse notch 160 mm from bearing 1 along the length of thelonger shaft. The Electro Discharge Machining (EDM) process was used to producethe notch. Misalignment fault was introduced at C1 by positioning 0.8 mm shimsunder either side of B1 pedestal (that is between the base of the pedestal and thelathe bed). Rub condition was simulated using a rub apparatus. Full annular rub wasproduced. Experiments were performed at a steady state rotational speed of2,400 rpm (40 Hz). Vibration data were acquired for 20 samples in a total combined

Vibration-Based Condition Monitoring for Rotating… 121

sample time of 2 min using a sampling frequency of 10,000 Hz. After data col-lection was complete for the FS1 set up, all four bearings on the rig were recon-figured to the FS2 set up and the experiments were repeated.

Fig. 1 Schematic of experimental rig with instrumentation

Fig. 2 Close up of: a front elevation of rigid coupling area showing bearing supports with flexiblerods and b end elevation of bearing assembling showing 45° accelerometer mount


3 Comparison of Dynamic Behaviour

Figure 3a, b present samples of the amplitude spectra that were computed for thebaseline RMRU condition at Bearing 2 for both FS1 and FS2 when operated at2,400 rpm (40 Hz) respectively. For FS1 a fundamental frequency at 39.37 Hz isseen in addition to 2x and 3x harmonic components. The presence of the 2x and 3xharmonic components suggest that this condition is characterised by some residualmisalignment. The conspicuous peak observed to the right of the 3x harmoniccomponent was generated due to the excitation of the 127 Hz natural frequency ofFS1. Similar observations were made from the spectral plot of FS2, where a distinctfundamental frequency of 39.37 Hz is observed in addition to multiple harmoniccomponents of the fundamental frequency up to the 6x harmonic. The peaks seen inclose proximity to the 3x and 4x harmonic components were due to excitation of therig’s natural frequency when configured to FS2. Again, there is some in builtmisalignment with rig for FS2.

The observations made here is typical of all the spectra generated for the dif-ferent cases tested for FS1 and FS2. By extension, the observations made in thissection demonstrate what is typical of machines with different natural frequencies,in that they may possess different features due to the excitation of the naturalfrequencies by the harmonics present in the spectrum even when the machine states(health) are the same. Then again, it is widely accepted that different faults maygenerate similar features [10]. Such is the nature of diagnosis with the simpleamplitude spectra, which is why a skilled and experienced analyst and moreadvanced processing techniques are usually needed to extract some useful infor-mation from the measured vibration data that can lead to diagnosis [5].

0 1X 2X 3X 4X 5X 6X0

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Fig. 3 Amplitude spectra for RMRU at B2 for a FS1 and b FS2


4 Data Analysis and Processing

Pattern recognition by Principal Component Analysis (PCA) adopted earlier [8, 9]was again used for diagnosis in the present study. PCA is an orthogonal lineartransformation that is used to reduce the dimensionality of a data set by trans-forming to a new set of ordered and uncorrelated variables called Principal Com-ponents (PCs), which retain as much variation present in all the original data set [6].Therefore, for each support tested PCA is used to transform a matrix of the healthindicators computed from the samples of each bearing of each of the four scenariostested (RMRU, crack, misalignment and rub) to a set of PCs [7] for interpretation.To produce an accurate representation of the state of the machinery, the featuresused in the present study are root mean square (r.m.s.) amplitude, amplitudes of the1x, 2x, 3x, 4x and 5x harmonic components and the spectrum energy (S.E.). R.M.S.and S.E. give overall indications of the machine in the time and frequency domainsrespectively, while the amplitudes of the harmonic components can provide furtherinsight to the specific machine state present. Thus one time domain and six fre-quency domain features computed for each bearing location were applied. Thefeatures derived from the frequency domain were computed using; 16,384 spectrallines, high pass filtering at 5 Hz, a Hanning window and 35 averages.

Transformation of the features to the Principal Component space is done using aMatlab algorithm in which the matrix of features (health indicators) is input forprocessing. The input feature matrix for the independent analysis consists of 28rows and 80 columns while that for the combined analysis consist of data from FS1and FS2 combined in a single matrix of 56 rows and 80 columns.

5 Fault Diagnosis

5.1 Independent Analysis of Flexible Supports, FS1 and FS2

The results of the independent analysis on FS1 and FS2 are shown in Figs. 4 and 5respectively. Figure 4a, b show two dimensional representations of the secondprincipal component (PC2) versus the first principal Component (PC1) [8, 9] in fullview and in a zoomed view respectively. The full view shows clear separation ofthe rub and crack cases from the RMRU condition. It did not seem like the tech-nique was able to discriminate the misalignment case from the RMRU condition.However, on closer inspection of the apparent overlap between RMRU and themisalignment cases in Fig. 4b, good separation is observed between the two cases.Similar observations were made for FS2 as shown in Fig. 5a, b. However, it wasnoted that less separation was seen between all the faulty cases and the RMRU casefor FS2 when compared to FS1. Decreased separation between the differentcases suggests there are stronger correlations between the sets of data used in theanalysis [6].


5.2 Combined Analysis of FS1 and FS2

The results of the combined analysis are shown in full view and zoomed view inFig. 6a, b respectively. From a cursory glance of the full view of the results, thedifferent fault conditions tested were conspicuously separated from the RMRUbaseline condition and also from each other. There was also better clustering of thedifferent conditions which is desirable for the diagnosis process [8, 9]. It wasapparent that the combined approach was able to utilise the information availablefrom the two separate support types to improve its efficacy. This analysis is based

-6 -4 -2 0 2 4 6 8 10 12-6

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Fig. 4 Fault diagnosis of FS1 at a full view and b zoomed view

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Fig. 5 Fault diagnosis of FS2 at a full view and b zoomed view


on the recognition of patterns (similarities and differences) between the sets of datafrom the FS1 and FS2. The current analysis differs from vibration analysis in thetraditional sense where the amplitude spectrum for a given fault condition on agiven machine is interrogated for changes relative to a previously acquired “heal-thy” baseline condition from that said machine [2]. Since “traditional” vibration-based condition monitoring requires comparison with a baseline condition itbecomes apparent that the baseline amplitude spectra have to remain constant.Consequently, if the amplitude spectra of the baseline conditions differ, thencomparison of vibration data between machines with different dynamic character-istics would not be useful for condition monitoring.

6 Conclusion and Future Work

The transferability of a previously developed FD technique to rotating machineswith relatively flexible supports was investigated in the current study. A FD methodfor comparing vibration data from similar machines with different flexible supportswas also introduced. The experimental rig used to develop the previous techniquehad relatively rigid supports and was modified to accommodate two different rel-atively flexible supports. Samples of on-bearing vibration data were acquired for abaseline residual misalignment with residual unbalance (RMRU), crack, mis-alignment and rub conditions and used to compute machine health indicators forfurther processing. Three sets of processing were done: previous technique on FS1,previous technique on FS2 and the newly introduced combined technique on FS1and FS2. The previously proposed technique gave good separation between the

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Fig. 6 Fault diagnosis with combined classification of FS1 and FS2 in a full view and b zoomedview


conditions tested and is therefore confirmed insensitive to the support types used.The result of the newly introduced combined analysis gave improved separationbetween the different conditions tested and thus may be useful in a practical casewhere similar machines are installed at different sites. There is need for furthervalidation of both techniques with additional faults.

Acknowledgments A.D. Nembhard sincerely thanks the Commonwealth Scholarship and Fel-lowship Plan (CSFP) for affording him the Ph.D. Commonwealth Scholarship to the UK fromJamaica to pursue the current research.

References

1. British Standards Institution (2007) BS ISO 13374-2:2007 Condition monitoring anddiagnostics of machines—data processing, communication and presentation—Part 2: dataprocessing. BSI, London

2. British Standards Institution (2009) BS ISO 10816-1:1995 + A1:2009 Mechanical vibration—evaluation of machine vibration by measurement of on non-rotating parts—Part 1: generalguidelines. BSI, London

3. Elbhbah K, Sinha JK (2013) Vibration-based condition monitoring of rotating machines usinga machine composite spectrum. J Sound Vib 332(11):2831–2845

4. Ewins DJ (2000) Modal testing—theory, practice and application, 2nd edn. Research StudiesPress, Baldock

5. Jardine AKS, Lin D, Banjevic D (2006) A review of machinery diagnostics and prognosticsimplementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510

6. Jolliffe IT (2002) Principal component analysis, 2nd edn. Springer, New York7. Li W, Shi T, Liao G, Yang S (2003) Feature extraction and classification of gear faults using

principal component analysis. J Qual Maint Eng 9(2003):132–1328. Nembhard AD, Sinha JK, Pinkerton A, Elbhbah K (2013) Fault diagnosis of rotating machines

using vibration and bearing temperature measurements. Diagn Appl Struct Usage Cond Monit14(3):45–51

9. Nembhard AD, Sinha JK, Pinkerton AJ, Elbhbah K (2013b) Combined vibration and thermalanalysis for the condition monitoring of rotating machinery. Struct Health Monit 13(3):281–295

10. Patel TH, Darpe AK (2009) Experimental investigations on vibration response of misalignedrotors. Mech Syst Signal Process 23(7):2236–2252


[mechanisms and machine science] vibration engineering and technology of machinery volume 23 ||...

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