Induction Motor Diagnostic System Based on Spectra Analysis

of Current and Instantaneous Power Signals

Mykhaylo Zagirnyak, SMIEEE, Dmytro Mamchur, Andrii Kalinov Kremenchuk Mykhailo Ostrohradskyi National University

Kremenchuk, Ukraine e-mails: mzagirn@kdu.edu.ua, dm@kdu.edu.ua, scenter@kdu.edu.ua

Abstract—In this paper, the theoretical basis and developed hardware and software for an induction motor diagnostic system based on electrical signals analysis is presented. The theoretical principles underlying these methods are based on a spectral analysis of the stator current and motor power consumption signals. The hardware allows data measuring for different operating modes with different range of motor loading. The software provides analysis of preliminary recorded or on-line collecting data using motor current signature analysis (MCSA) and instantaneous power spectra analysis (IPSA) methods. Experimental tests with real data analysis showed the possibility of utilization developed methods and equipment for induction motor diagnostic systems.

Keywords—induction motors, diagnostics, instantaneous power, monitoring, motor current signature analysis

I. INTRODUCTION Recent researches showed that induction motors (IM)

consume over 80 % of the total amount of electric power, so they are the most widely spread consumers of electric energy [1]. Their wide spread occurrence in industry is explained by their simple construction, high reliability and relatively low cost. Moreover, IM could be easily implemented into speed-controlled electric drives, which are the main unit of industrial automation systems nowadays. Thus, reliable operation of IM is a main condition of trouble-free operation of entire technological process. Unfortunately, different damages can be caused to IM parts under operating conditions. As result, every year about 20–25 % of IM fail. Of course industrial enterprises provide planned preventive maintenance (PM) in order to avoid emergency. But even these operations do not prevent sudden failures or undesirable operation modes under unsatisfactory technical conditions during between-maintenance periods, especially for low- and medium-voltage motors. In order to eliminate shortcomings of PM it was proposed to implement condition based maintenance (CBM). Its goal is to maintain equipment when it needs maintenance, basing on signals from diagnostic indicators. This technique is preferable in order to predict a failure and to plan repairing expenses, but it requires additional measuring equipment to control diagnostic indicators. In tasks of incipient IM faults detection, the most convenient diagnostic methods operate with electrical signals

as diagnostic indicators. These methods are attractive because of measuring simplicity, they do not require expensive measuring equipment, and have well-developed theoretical background. There are two most appropriate methods for low- and medium-voltage motors diagnostics among them: motor current signature analysis (MCSA) [2] and instantaneous power spectra analysis (IPSA) [3, 4]. First mentioned method requires only one-phase current signal for analysis. Second method requires all phase currents and voltages signals for analysis. Last method is more complicated; however, it gives more reliable results. This paper presents IM diagnostic system based on MCSA and IPSA with comparative analysis of the results provided by these methods.

II. MOTOR CURRENT SIGNATURE ANALYSIS (MCSA) Due to simplicity of data acquisition in operation mode,

MCSA became a widely used IM diagnostic method. MCSA has a well-developed theoretical background, and there are a lot of works devoted to its implementation for detecting most frequently caused IM fault types, namely the stator windings short-circuits, the rotor imbalances and rotor bar breaks, and also the bearings damages [2]. The concept of this method is correspondence of IM faults to certain harmonics in current spectra, as it is shown below.

A. Broken rotor bars When motor rotor bars breaks, the disturbance induces

fundamental sidebands of the supply frequency in the current signal [2]:

s21ff sbb

where sf is the supply fundamental frequency, s is the motor slip.

B. Air gap eccentricity Non-uniform air gap results in occurrence of two types of

unique frequencies in current spectrum: around fundamental harmonic ( eccen_funf ) and around principle slot harmonic

( eccen_prinf ) [2]:

978-1-4799-6585-4/14/$31.00 ©2014 IEEE

rseccen_fun fkff

where p

s1fr

, where s is the motor slip,

p is the number of pole pairs;

sdeccen_prin fp

s1nRkf

where R is the number of rotor slots; k is the positive integer number; dn is an integer conditioned by dynamic eccentricity;

is the time harmonic of the motor supply.

C. Stator windings shor circuits Stator shorted turns occurs as result of insulation

degradation or damage. Thus, they may lead to unpredictable sudden failure. That’s why detection of their inception is a task of main importance. Previous studies allowed define harmonic components in the airgap flux waveform which are function of shorted turns and are not due to any other faults [5, 6]:

ks1

pnff sst

where sf is the supply main frequency;

n is a positive integer number (1, 2, 3…);

s is the motor slip;

k can be equal to 1, 3, 5 or 7.

These components induce corresponding components in stator current waveform.

D. Bearing damage Rotation of damaged bearings leads to vibrations. As

bearings support the rotor their vibrations lead to air gap fluctuations which, in their turn, give current components expressed by [2]:

o,isbrg fmff

where 1,2,3...m , and oi,f is one of the characteristic vibration frequencies, which are based upon the bearing dimensions:

cos

pdbd1f

2nf voi,

where n is the number of bearing balls;

vf is the mechanical rotor velocity in hertz;

bd is the ball diameter;

pd is the bearing pitch diameter;

is the contact angle of the balls on the races.

Analysis of equations (1)–(5) shows, that all described damage types may generate harmonic components in current signals with identical or similar frequencies. In order to separate diagnostic features of different fault types, additional analysis may be used, such as vibration analysis or more complicated mathematical apparatus. However, in case of supply voltage low quality with significant unsinusoidality, even this additional analysis does not prevent diagnostic mistakes, because voltage distortion harmonics may lead to current harmonics with the faulty frequencies. This fact is extremely significant for low-voltage induction motors.

III. INSTANTANEOUS POWER SPECTRA ANALYSIS (IPSA) Instantaneous power spectra analysis allows avoiding

above mentioned shortcomings of the MCSA [3, 4, 7–9]. IPSA provides both fault detection and estimation of probable damage level via analysis of proper harmonic value. Thus, it makes possible to estimate the fault energy and correlation of this energy to additional damage of IM units under influence of additional vibrations caused by proper harmonic components. Furthermore, IPSA allows analyzing IM in operation modes with significant nonlinearity, when it is incorrect to apply superposition principle for current harmonics. Finally, this method is less dependent on noise because of higher value of fault-related harmonic amplitudes comparing to noise harmonics, and also it gives additional harmonic components for analysis [3, 4].

The instantaneous power could be described by following expression:

titutp

where tu is the phase voltage;

ti is the input phase current.

In the case of healthy motor fed from ideal supply and running with constant velocity, the expressions of the phase voltage tu , phase current ti and instantaneous power could be described as [10, 11]:

tcosU2tu 1

tcosI2ti 1

,t2sinsinIU

t2coscosIUcosIUsintsin

costcostcosIU2

tcostcosIU2titutp

11

1111

11

11

6

978-1-4799-6585-4/14/$31.00 ©2014 IEEE

where 11 I,U are RMS values of phase voltage and current, respectively;

sf2 is the angular frequency, where sf is the supply frequency;

is the motor load angle.

In difference to current spectrum, with its only fundamental component at the frequency sf , the instantaneous power spectrum, except for fundamental component at frequency s2f , also contains constant power (CP) component cosIU 11 .

In order to make a comprehensive IM analysis and fault diagnostics, the total instantaneous power of three phases, which is the sum of phase instantaneous powers, may be analyzed additionally:

titutitutitutp CCBBAAtot

This signal contains more diagnostic information: in addition to detection previously described faults, which lead to electrical signals modulation with determined frequencies, total three phase power signal allows detection and analysis of faults caused by motor or supply asymmetry. In case of healthy motor fed by symmetrical sinusoidal supply, three phase power spectrum, due to compensation of symmetrical harmonic components, contains only CP component. Thus, every kind of motor fault or drive system asymmetry leads to appearance of unique harmonic components related to certain fault type, and they are additional diagnostic feature.

A. Rotor bar breaks According to (1), breakage of rotor bars causes sinusoidal

modulations of the stator current. By analogy to [9], the modulated phase current may be described by following expression:

tff2costff2cos

II22ti

tf2cosI1titi

bbs

bbsm1

bbmm

7

where mI is the modulation index;

bbf is the modulating frequency;

s is the motor slip.

According to (7), phase current spectrum, additionally to fundamental component, contains two sideband components at frequencies bbs ff and bbs ff .

Modulated phase instantaneous power could be described by the following expression:

.t2cos

tff2cosUIItff2cosUII

t2sinsinIUt2coscosIUP

tutitp

bbs1m1

bbs1m1

11

110

mm

This expression shows that phase instantaneous power spectrum, besides CP component 0P and two sideband components at frequencies bbs ff2 and bbs ff2 , additionally contains component with amplitude value of

tfcoscosUII bb1m1 at the modulation frequency bbf , which is an additional diagnostic feature.

B. Air gap eccentricity Air gap eccentricity lead to sinusoidal modulations of phase

currents with the frequencies expressed by (2):

,

tkff2cosItkff2cosI

I22

titiK

1k k2ersk2e

k1ersk1e1

eccen

9

where k1eke1 ,I are the current modulation amplitudes and the initial phase angle for frequencies rs kff ;

k2eke2 ,I are the current modulation amplitudes and the initial phase angle for frequencies rs kff .

In this case, modulated phase power will be expressed as:

.

tkff2cosItkff2cosI

t2cosU2

t2sinsinIUt2coscosIUPtutitp

K

1k k2ersk2e

k1ersk1e1

11110

ecceneccen

10

Expression (10) shows, that in case of air gap eccentricity phase power spectrum contains sideband components at frequencies rs kff2 and rs kff2 , and also additional harmonic components at frequencies rkf .

C. Stator windings short circuits Sinusoidal current modulation with frequencies expressed

by (4) caused by short circuits in stator windings, may be described as:

tf2cosII22ti

tf2cosI1titi

stm1

stmm 11

978-1-4799-6585-4/14/$31.00 ©2014 IEEE

where mI is the modulation index;

stf is the modulating frequency, according to (4);

s is the motor slip.

Expression (11) shows, that phase current spectrum, additionally to supply frequency harmonic, contains harmonics at frequencies stf .

In case of this fault, sinusoidal modulations of phase power signal will be described as:

.t2costf2cosUII21

t2sinsinIUt2coscosIUPtutitp

st1m1

11

110mm

12

According to expression (12), short circuits in stator windings lead to harmonics at modulating frequencies stf and their combinations with the supply frequency harmonics in phase power spectrum.

D. Bearings damage Bearings damages lead to vibration, which causes current

modulation at frequencies according to (5):

,

tkff2cosI

tkff2cosII

22

titi

K

1k k2bbrgsk2b

k1bbrgsk1b1

brg

(13)

where k1bkb1 ,I are the current amplitudes and the initial phase angles for frequencies brgs kff ,

k2bkb2 ,I are the current amplitudes and the initial phase angles for frequencies brgs kff .

Motor phase power in case of bearings damage, will be expressed as the following:

.

tkfcoscosI

tkfcoscosI

tkff22cosI

tkff22cosI

UI21tp

tutitp

K

1k

brgk2bk2b

brgk1bk1b

k2bbrgsk2b

k1bbrgsk1b

110

brgbrg

(14)

Last expression shows, that in case of bearings faults motor phase power spectrum, in addition to supply frequency harmonic, contains sideband components at frequencies

brgs kff2 and brgs kff2 , and also additional harmonic components at frequencies brgkf .

IV. DEVELOPMENT OF THE EXPERIMENTAL SAMPLE OF DIAGNOSTIC EQUIPMENT

Basing on the demands of previously described diagnostic methods, special diagnostic equipment was developed. This equipment consists of two parts: hardware for measuring and collecting electrical signals; and software for analyzing collected data according to both described diagnostic methods and presenting diagnostic results to users. In this chapter developed equipment, software and tested samples of artificially damaged induction motors is presented.

A. Hardware development In order to measure and collect electrical signals which are

necessary for the analysis basing on MCSA and IPSA, it was developed the measuring unit and measuring software (Fig. 1, Fig. 2).

Fig. 1. Measuring unit functional circuit (SB – sensor block; VB – voltage block; VRD – voltage resistance divider; GIA – galvanic isolation amplifier; CS – current sensor; PC – personal computer; PACA – programmed amplification coefficient amplifier; USB – PC bus)

Measuring ModuleTested Motor

Measuring-and-Diagnostic Software

Fig. 2. Photo of the measuring complex components

To verify developed diagnostic system, a series of tests with artificially damaged motors were done. The most frequently caused IM fault types were took into consideration for analysis, namely, broken rotor bars, short circuits in stator windings and rotor eccentricity. For investigation, three identical induction motors of type AIR80V4U2, 1.5 kW were used. Because of simplicity of induction motor construction and possibility to interchange IM units, it is possible to

978-1-4799-6585-4/14/$31.00 ©2014 IEEE

artificially damage separately stators (Fig. 3) and rotors (Fig. 4), and then combine them for assembling motor with different damage types and degree of this damage.

For imitation motor stator faults, namely, turn-to-turn short circuits, the taps were provided in one of the stator winding phases (Fig. 3). Stator phase short circuits causes change of stator winding resistance, and consequently lead to phase asymmetry of stator winding (Table I).

Rotor bar breaks were imitated by breaking the contact of the bar and cage ring via drilling apertures in places, where bars are attached to rings. To investigate progress of this fault, it was used several identical interchangeable rotors with 1, 2 or 3 broken bars.

The DC generator provides a mechanical load. The motor speed is controlled via encoder. Asymmetry of supply voltage could be achieved using the voltage transformer in one of the supply phases.

Fig. 3. Stator phase winding taps circuit

TABLE I. IM WINDING RESISTANCE MEASUREMENT DATA

Phase Resistance value, ohm А 7.576 В 7.632 С 7.66

Winding part

Resistance value, ohm

Reduction of winding turns

number, % 1-z 7.45 2.74 2-z 6.9 10

Taps in phase С

3-z 6.31 17.6

Fig. 4. Scheme of rotor apertures location

B. Software Development Basing on previously defined expressions (7)–(14), the

diagnostic software was developed. This software provides analysis basing both on MCSA and on IPSA methods. User interface is presented in Fig. 5–8. This software allows operation both with previously collected data and with online

collecting data (Fig. 5). User may define a number of signal periods for analysis. The bigger number of periods is the more precise harmonic frequency value could be achieved. However, this leads to increase of processing time. So user may adjust this option in accordance with his needs: more precise or faster analysis. Basing on collected data, software calculates Fast Fourier Transform (FFT) and shows currents, voltages and power spectra for each phase (Fig. 6) and for total three-phase instantaneous power (Fig. 7). According to developed diagnostic algorithms, which are based on MCSA and IPSA, program generates diagnostic results (Fig. 8).

Fig. 5. Read data and signal settings window

rotor bar breaks

rotor bar breaks

Fig. 6. Spectra analysis for phase electric signals

rotor bar breaks

stator and supply unsymmetry

supply unsinusoidality

Fig. 7. Spectra analysis for total 3-phase instantaneous power signal

978-1-4799-6585-4/14/$31.00 ©2014 IEEE

Fig. 8. Diagnostic results window

V. EXPERIMENTAL RESEARCH In order to verify developed diagnostic system, a range of

experimental tests was done. Correspondence of the motor damage types to the experiment sequence number is shown in Table II (additionally see Fig. 3, Fig. 4, Table I). During experimental research the following assumptions were accepted.

Motor operates with a constant load. Load variation may lead to appearance of interharmonics and low-frequency harmonics in power and current spectra. Interharmonics do not influence significantly on faults related harmonics, while the influence of low-frequency harmonics could be taken into account analyzing mean value of total three-phase motor power.

TABLE II. EXPERIMENTS WITH IM ARTIFICIAL DAMAGES

No. Fault type 1 IM basic variant without artificial damages (healthy motor) 2 IM with one broken bar (bar #1) 3 IM with one broken bar (bar #1) and phase C winding short circuit

2.74% 4 IM with one broken bar (bar #1) and phase C winding short circuit

10% 5 IM with one broken bar (bar #1) and phase C winding short circuit

17.6 % 6 IM with two broken bars (bars #1 and #2) 7 IM with two broken bars (bars #1 and #2) and winding short circuit

2.74% 8 IM with four broken bars (bars #1, #2, #3 and #4)

Motor heating influences on the change of phase active resistances. Uniform heating cause symmetrical change of these resistances in each phase. Non-uniform heating leads to clearer phase asymmetry of harmonic amplitude values by phases.

Preliminary researches showed that saturation causes appearance of 6-th and its multiple harmonics in instantaneous power spectrum. This fact will not influence on diagnostic results, because proposed methods operates with lower frequencies.

Also it should be mentioned, that developed diagnostic system with its software could be successfully used both for variable and fixed speed induction motors.

Phase currents and voltages were collected both under idle and full load modes, and then they were analyzed. Experimental data is shown in Fig. 9 and Fig. 10.

a) Experiment #1: Healthy motor b) Experiment #5: 17.6% of stator phase asymmetry

c) Experiment #6: Two broken rotor bars d) Experiment #7: Two broken rotor bars

and 2.54% of stator phase asymmetry Fig. 9. Phase current

978-1-4799-6585-4/14/$31.00 ©2014 IEEE

a) Experiment #1: Healthy motor b) Experiment #5: 17.6% of stator phase asymmetry

c) Experiment #6: Two broken rotor bars d) Experiment #7: Two broken rotor bars and 2.54% of stator phase asymmetry

Fig. 10. Phase power

Experimental data analysis gives the following results. Basically, all tested motors have mixed eccentricity. It could be explained by motor disassembling and assembling operations during test preparation. All tested damage types could be detected using both MCSA and IPSA methods. However, amplitude values of, for example, current spectrum harmonics related to motor stator asymmetry, are too small (Fig. 9, b, d). So in case of incipient fault they could be wrong identified as a noise harmonics. In difference to this method, IPSA allows operate with clearly visible harmonics (Fig. 10, b, d). Also, this method provides a number of additional diagnostic harmonics for analysis. This feature allows avoiding wrong diagnosis. Thus, experimental verification has shown that IPSA is more reliable diagnostic method, especially in case of incipient fault detection for motor, operating in idle mode, when amplitude values of fault-related harmonics in current spectrum are too small and could be wrong identified as noise harmonics.

VI. CONCLUSION A necessity of development novel easy-to-use and reliable

induction motor diagnostic system was grounded in this paper. Proposed system is based on motor electrical signals analysis. Such kind of analysis allows us create cheap hardware, because of the simplicity of data acquisition, and also provides reliable result because of well-developed and tested theoretical background. Developed software provides IM diagnostic basing on motor current and power spectra analysis. Experimental verification of developed diagnostic system showed possibility of successful detection of most frequently caused IM damage types using both current and power spectra analysis. However, motor power spectra analysis gives more reliable results due to additional diagnostic features and more clearly detectable diagnostic harmonics, which is essential especially for idle operation modes and in case of incipient faults. Developed diagnostic system is a ready-to-use system, and could be implemented in industry for monitoring and diagnosis of small and middle power induction motors.

REFERENCES [1] D. Mamchur, “An instantaneous power spectra analysis as a method for

induction motors fault detection,” Proceedings of OWD’2011, 22–25 October 2011, Wisla, pp. 407-412, ISBN 83-922242-4-0.

[2] M. E. H. Benbouzid, “A review of induction motors signature analysis as a medium for faults detection,” IEEE Transactions on Industrial Electronics, vol. 47, no. 5, pp. 984–993, Oct. 2000.

[3] M. V. Zagirnyak, D. G. Mamchur, A. P. Kalinov, “Comparison of induction motor diagnostic methods based on spectra analysis of current and instantaneous power signals,” Przeglad Elektrotechniczny, Iss. 12b/2012, pp. 221–224.

[4] M. V. Zagirnyak, D. G. Mamchur, A. P. Kalinov, “Elimination of the influence of supply mains low-quality parameters on the results of induction motor diagnostics,” in Proc. XIX International Conference on Electrical Machines - ICEM 2010, Rome. IEEE Catalog Number: CFP1090B-CDR. ISBN: 978-1-4244-4175-4. Library of Congress: 2009901651. RF-009474.

[5] W. T. Thomson, “On-Line MCSA to Diagnose Shorted Turns in Low Voltage Stator Windings of 3-Phase Induction Motors Prior to Failure”, IEEE, PES&IAS IEMDC, MIT, Boston, June, 2001, pp. 891–898.

[6] Neelam Mehala and Ratna Dahiya, “Motor current signature analysis and its applications in induction motor fault diagnosis,” International Journal of Systems Applications, Engineering & Development, Vol 2, 2007, pp. 29–35.

[7] S. F. Legowski, A. H. M. Sadrul Ula and A. M. Trzynadlowski, “Instantaneous power as a medium for the signature analysis of induction motors,” IEEE Transactions on Industrial Electronics, vol. 32, no. 4, pp. 904–909, Jul./Aug. 1996.

[8] A. M. Trzynadlowski and E. Ritchie, “Comparative investigation of diagnostic media for induction motors: a case of rotor cage faults,” IEEE Transactions on Industrial Electronics, vol. 47, no. 5, pp. 1092–1099, Oct. 2000.

[9] M. Drif and A. J. M. Cardoso, “The use of the instantaneous-reactive-power signature analysis for rotor-cage-fault diagnostics in three-phase induction motors,” IEEE Transactions on Industrial Electronics, vol. 56, no. 11, pp. 4606–4614, Nov. 2009.

[10] D. J. Rodkin, “New system of electrical energy usage quality indicators,” Scientific Herald of National Mining University, 2004, vol. 3. pp. 20–26 (In Russian).

[11] Wang Li, Wang Xuan, Wei Min, “Motor bearing fault diagnosis based on wavelet packet decomposition of instantaneous power,” in Proc. International Conference on Computer Design and Applications (ICCDA 2010), pp. V3-457 – V3-459.

978-1-4799-6585-4/14/$31.00 ©2014 IEEE