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14 IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 14, NO. 1, JANUARY 1999

Induction Motors’ Faults Detection and LocalizationUsing Stator Current Advanced Signal

Processing TechniquesMohamed El Hachemi Benbouzid, Member, IEEE, Michelle Vieira, and Celine Theys

 Abstract— The reliability of power electronics systems is of paramount importance in industrial, commercial, aerospace, andmilitary applications. The knowledge about fault mode behaviorof an induction motor drive system is extremely important fromthe standpoint of improved system design, protection, and fault-tolerant control. This paper addresses the application of motorcurrent spectral analysis for the detection and localization of ab-normal electrical and mechanical conditions that indicate, or maylead to, a failure of induction motors. Intensive research efforthas been for some time focused on the motor current signatureanalysis. This technique utilizes the results of spectral analysisof the stator current. Reliable interpretation of the spectra isdifficult since distortions of the current waveform caused bythe abnormalities in the induction motor are usually minute.This paper takes the initial step to investigate the efficiencyof current monitoring for diagnostic purposes. The effects of stator current spectrum are described and the related frequenciesdetermined. In the present investigation, the frequency signatureof some asymmetrical motor faults are well identified usingadvanced signal processing techniques, such as high-resolutionspectral analysis. This technique leads to a better interpretationof the motor current spectra. In fact, experimental results clearlyillustrate that stator current high-resolution spectral analysis isvery sensitive to induction motor faults modifying main spectralcomponents, such as voltage unbalance and single-phasing effects.

 Index Terms—Broken bars, damaged bearings, fault detec-tion, induction motors, rotor eccentricity, shaft speed oscillation,

single-phasing effects, spectral analysis, stator current, unbal-anced voltage.

I. INTRODUCTION

IN GENERAL, condition monitoring schemes have concen-

trated on sensing specific failures modes in one of three

induction motor components: the stator, rotor, or bearings.

Even though thermal and vibration monitoring have been uti-

lized for decades, most of the recent research has been directed

toward electrical monitoring of the motor with emphasis on

inspecting the stator current of the motor. In particular, a

large amount of research has been directed toward using thestator current spectrum to sense rotor faults associated with

Manuscript received May 10, 1997; revised May 28, 1998. This work was supported by the French Concerted Research Program (PRC) on theDiagnostics of Electrical Machines. Recommended by Associate Editor,M. Arefeen.

M. E. H. Benbouzid is with the University of Picardie “Jules Verne,” 80000Amiens, France (e-mail: m.benbouzid@ieee.org).

M. Vieira and C. Theys are with the Laboratoire d’Informatique, Signauxet Systemes, CNRS & University of Nice “Sophia Antipolis,” 06041 Nice,France.

Publisher Item Identifier S 0885-8993(99)00282-3.

broken rotor bars and mechanical unbalance [1]–[7]. All of 

the presently available techniques require the user to have

some degree of expertise in order to distinguish a normal

operating condition from a potential failure mode. This is

because the monitored spectral components (either vibration

or current) can result from a number of sources, including

those related to normal operating conditions. This requirement

is even more acute when analyzing the current spectrum of an

induction motor since a multitude of harmonics exist due to

both the design and construction of the motor and the variationin the driven load. Many of these harmonics can be caused by

ovalities in the rotor, voids in the casting, slot design, etc.

Condition monitoring of the dynamic performance of elec-

trical drives received considerable attention in recent years.

Many condition monitoring methods have been proposed

for different types of rotating machine faults detection and

localization [8]–[14].

Large electromachine systems are often equipped with me-

chanical sensors, primarily vibration sensors based on prox-

imity probes. Those, however, are delicate and expensive.

Moreover, in many situations, vibration monitoring methods

are utilized to detect the presence of incipient failure. How-

ever, it has been suggested that stator current monitoring can

provide the same indications without requiring access to the

motor [3]. Therefore, we have focused our research on the so-

called motor current signature analysis. This technique utilizes

results of spectral analysis of the stator current (precisely, the

supply current) of an induction motor to spot an existing or

incipient failure of the motor or the drive system. It is shown

that the amount of information brought by the use of advanced

signal processing techniques, such as high-resolution spectral

analysis, is lower than that deducible from the use of classical

spectral analysis property, but allows obtaining a characteristic

spectral signature which can be easily distinguished from a

normal operating condition and then identified as a potentialfailure mode. Of particular interest are broken bars in the rotor

cage, rotor eccentricity, worn or damaged bearings, shaft speed

oscillation, and electrical-based faults (unbalanced voltage and

single-phasing effects) [15]–[20]. Experimental investigations,

for high-resolution analysis, have been carried out only for

electrical-based faults detection and localization.

II. STATOR CURRENT SIGNATURE ANALYSIS

A current spectra contains potential fault information. Fre-

quency components have been determined for each specified

0885–8993/99$10.00 © 1999 IEEE

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BENBOUZID et al.: INDUCTION MOTORS’ FAULTS DETECTION AND LOCALIZATION 15

fault. These frequencies are derived from the physical con-

struction of the machine. It is important to note that, just as

in vibration analysis, as the fault progresses, its characteristic

spectral components continue to increase over time.

 A. Eccentricity and Broken Bars

A broken bar may be distinguished from an asymmetry

by examining the harmonics sidebands. An asymmetry willtypically result in a smooth variation of air-gap flux density. It

has been shown that both rotating and nonrotating eccentricity

will give rise to current components at frequencies given by [2]

(1)

where is the electrical supply frequency, ,

the rotor bars number, the eccentricity order number,

the per unit (p.u.) slip, the number of pole pairs, and

the supply frequency harmonic rank.

 B. Shaft Speed Oscillation

In the case of dynamic eccentricity that varies with rotor

position, the oscillation in the air-gap length causes variations

in the air-gap flux density. This, in turn, affects the induc-

tance of the machine producing stator current harmonics with

frequencies predicted by [3]

(2)

C. Rotor Asymmetry

It has been shown that when a rotor asymmetry is present,

the air-gap flux density will be perturbed and this perturbationwill rotate at shaft speed. The frequencies of the spectral

components in the air-gap flux density are given by [2]

(3)

 D. Bearings Failure

Installation problems are often caused by improperly forcing

the bearing onto the shaft or in the housing. This produces

physical damage in the form of brinelling or false brinelling of 

the raceways which leads to premature failure. Misalignment

of the bearing, which occurs in the four ways depicted in

Fig. 1, is also a common result of defective bearing installa-tion.

The relationship of the bearing vibration to the stator current

spectra can be determined by remembering that any air-gap

eccentricity produces anomalies in the air-gap flux density.

Since ball bearings support the rotor, any bearing defect will

produce a radial motion between the rotor and stator of the

machine. The characteristic frequencies for ball bearings are

based upon the bearing dimensions shown in Fig. 2. They are

given by [5], [21]

(4)

Fig. 1. Four types of rolling-element bearing misalignment [5].

Fig. 2. Ball-bearing dimensions.

with

(5)

where is the number of balls, the mechanical rotor speed

in hertz, the bearing pitch diameter, the ball diameter,

and the contact angle of the balls on the races.

III. STATOR CURRENT MONITORING SYSTEM

The stator current monitoring system contains the four

processing sections illustrated in Fig. 3.

  A. Sampler 

The purpose of the sampler is to monitor a single phase

of induction motor current. This is accomplished by removing

the 50-Hz excitation component through low-pass filtering and

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16 IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 14, NO. 1, JANUARY 1999

Fig. 3. Block diagram of single-phase stator current monitoring scheme.

sampling the resulting signal. The current flowing in single

phase of the induction motor is sensed by a current transformer

and sent to a 50-Hz notch filter, where the fundamental

component is reduced. The analog signal is then amplified and

low-pass filtered. The filtering removes the undesirable high-

frequency components that produce aliasing of the sampled

signal while the amplification maximizes the use of the analog-to-digital (A/D) converter input range. The A/D converter

samples the filtered current signal at a predetermined sampling

rate that is an integer multiple of 50 Hz. This is continued over

a sampling period that is sufficient to achieve the required fast

Fourier transform (FFT).

  B. Preprocessor 

The preprocessor converts the sampled signal to the fre-

quency domain using an FFT algorithm. The spectrum gen-

erated by this transformation includes only the magnitude

information about each frequency component. Signal noise that

is present in the calculated spectrum is reduced by averaginga predetermined number of generated spectra. This can be

accomplished by using either spectra calculated from multiple

sample sets or spectra computed from multiple predetermined

sections (or windows) of a single large sample set. Because of 

the frequency range of interest and the desired frequency res-

olution, several thousand frequency components are generated

by the processing section.

C. Fault Detection Algorithm

In order to reduce the large amount of spectral information

to a usable level, an algorithm, in fact, a frequency filter,

Fig. 4. View of the experimental setup.

eliminates those components that provide no useful failure

information. The algorithm keeps only those components that

are of particular interest because they specify characteristic fre-

quencies in the current spectrum that are known to be coupled

to particular motor faults. Since the slip is not constant during

normal operation, some of these components are bands in the

spectrum where the width is determined by the maximum

variation in the slip of the motor.

  D. Postprocessor 

Since a fault is not a spurious event, but continues to

degrade the motor, the postprocessor diagnoses the frequency

components and then classifies them (for each specified fault).

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BENBOUZID et al.: INDUCTION MOTORS’ FAULTS DETECTION AND LOCALIZATION 17

Fig. 5. Schematic view of the experimental setup.

Fig. 6. Full-loaded motor stator current power spectra.

IV. EXPERIMENTAL RESULTS

To verify the generality of the presented considerations,

laboratory experiments were performed with an inductionmotor. The investigated drive system is shown by Figs. 4 and

5. The stator current was sampled with a 1-kHz sampling rate

and interfaced to a Pentium PC by a National Instruments data

acquisition board. All of the following stator current power

spectra are based on a 4096-points FFT.

  A. Experiment 1

The first experiment involved the drive system driving the

full load at 1444 rpm. The power spectra of Fig. 6 represents

then, in our case, the supposed healthy motor. However,

when zooming the power spectra, as illustrated by Figs. 7

Fig. 7. Stator current power spectra around 50 Hz.

and 8, more frequency information appears. In fact, one could

identify in Fig. 7 eccentricity harmonics at 26 and 74 Hz [see(1)] and rotor asymmetry harmonics at 46 and 54 Hz [see

(3)]. In Fig. 8, the shaft speed oscillation harmonic appears

clearly at 246 Hz [see (2)]. When accurately analyzing the

power spectra, one could notice the absence of obvious bearing

failure.

  B. Experiment 2

In the second experiment, the stator voltages were unbal-

anced by adding a 0.2-p.u. resistance to one phase. The power

spectra of stator current is then shown by Fig. 9. One should

notice the emergence of even harmonics.

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20 IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 14, NO. 1, JANUARY 1999

Fig. 12. Power spectrum after pass-band filter at 1444 rpm.

Fig. 13. MUSIC frequency estimate for the healthy machine.

Fig. 14. MUSIC frequency estimate for the stator voltage unbalance.

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BENBOUZID et al.: INDUCTION MOTORS’ FAULTS DETECTION AND LOCALIZATION 21

spectral signatures illustrated by Figs. 13 and 14 can be easily

used for the detection and identification of a potential voltage

unbalance.

The frequency values obtained with the ROOT-MUSIC

estimator for and are 50.11 and 250.43 Hz

for the healthy motor and 50.08 and 150.22 Hz for the stator

voltage unbalance.

C. Discussions

With regard to these results, MUSIC and ROOT-MUSIC

methods allow keeping only the main frequencies without

other spectral information. This is a first step to an automated

diagnostic system.

The main advantages of the high-resolution spectral analysis

can be underscored in the last experiments:

1) taking the stator current model into consideration;

2) noise influence reduction;

3) extraction of the useful information (prefiltering and

choice of the number of sinusoids).

Even if the primary motivation for eigenanalysis-based

frequency estimators, i.e., the high-resolution property, has notbeen underscored in these experiments, this property can be

shown for lower slip values. In comparison with the classical

spectral analysis techniques, the eigenanalysis-based spectral

methods are computationally intensive. The approximate rela-

tive computational complexity of MUSIC and ROOT-MUSIC

algorithms is , where is the number of samples, whereas

classical spectral technique one is [24].

VII. CONCLUSIONS

This paper has taken the initial step to investigate the

efficiency of current monitoring for diagnostic purposes. The

effects of stator current spectrum have been described and

the related frequencies determined. In the present investiga-

tion, the frequency signature of some asymmetrical motor

faults have been well identified using advanced signal pro-

cessing techniques, such as high-resolution spectral analysis.

Experimental results have demonstrated that the stator current

high-resolution spectral analysis, proposed as a medium for

induction motors faults detection, has definite advantages

over the traditionally used FFT spectral analysis, and, more

generally, this technique will be useful in all faults modifying

main spectral components.

Extensive experimental studies are necessary to full assess

usefulness of the proposed technique for the preventive main-

tenance diagnostics and failure prevention in drive systemswith induction motors.

APPENDIX

RATED PARAMETERS OF THE MACHINE UNDER TEST

Power 4 kW

Frequency 50 Hz

Voltage 220/380 V

Current 15/8.6 A

Speed 1440 rpm

Pole pair 2

ACKNOWLEDGMENT

The authors thank A. Berrehilli and N. Farid for setting up

the experimental system. The valuable comments made by the

reviewers are also acknowledged.

REFERENCES

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Mohamed El Hachemi Benbouzid (S’92–M’94)

was born in Batna, Algeria, in 1968. He receivedthe B.Sc. degree in electrical engineering in 1990from the University of Batna, Batna, and the M.Sc.and Ph.D. degrees in electrical and computer en-gineering in 1991 and 1994, respectively, from theNational Polytechnic Institute, Grenoble, France.

After graduation, he joined the University of Picardie “Jules Verne,” Amiens, France, where he isan Associate Professor of Electrical and ComputerEngineering. His current research interests include

electric machines and drives, electromagnetics computational, and electro-mechanical actuation as well as techniques for energy savings. He is leadinga research program on induction machine drives monitoring and diagnosticsfor the French Picardie Region. He has published more than 40 technicalpapers including 15 refereed publications in journals.

Dr. Benbouzid is a Member of the French Society of Electrical EngineersSEE and is active in PES and IAS-IEEE.

Michelle Vieira was born in Belfort, France, in1972. She received the B.Sc. degree in telecommu-nications in 1994 from the University of Nancy-Metz, France, and the M.Sc. degree in signal pro-cessing in 1995 from the University of Nice, SophiaAntipolis, France. She is currently working towardthe Ph.D. degree in monitoring and diagnosis of induction motors via signal processing at the Uni-versity of Nice.

Celine Theys was born in Paris, France, in 1967.She received the B.Sc., M.Sc., and Ph.D. degreesin electrical engineering and digital signal process-ing from the University of Nice, Sophia Antipolis,France, in 1989, 1990, and 1993, respectively.

She is currently an Associate Professor at theUniversity of Nice. Her research interests includedigital signal processing for the detection of abruptchanges and fault diagnosis.