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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: [email protected]).
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.