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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 56, NO. 6, DECEMBER 2007
Diagnostic of a Faulty Induction Motor Drive
via Wavelet Decomposition
Ferdinanda Ponci, Member, IEEE, Antonello Monti, Senior Member, IEEE,
Loredana Cristaldi, Senior Member, IEEE, and Massimo Lazzaroni, Member, IEEE
Abstract—An approach to the analysis of ac side current is
presented for the purpose of identification of faults in the stator
phase resistance of an ac induction motor drive. The method relies
on the correlation between wavelet decomposition coefficients of
the current in healthy and faulty conditions. The findings highlight
that the fault causes waveform variations that are localized at
specific decomposition levels. The presented approach may open
the way to efficient training for fault recognition systems.
Index Terms—Diagnostic, measurements, pattern recognition,
testing, wavelet transform.
I. I NTRODUCTION
E
LECTRICAL and mechanical fault detection, identification, and isolation and remedial strategies in electrical
drives have been widely investigated. Methods relying on bearing vibration or thermal measurements have well-established
foundations. Recently, though, other techniques relying on electrical quantity measurements, signal processing, and artificial
intelligence techniques have been at the center of attention,
being noninvasive, potentially less expensive, and effective.
Different techniques have been proposed: from model-based
methods [1] to current signature analysis [2], [3] to finiteelement analysis [4]. Signal processing and artificial intelligence techniques have been applied for information extraction
and categorization and interpretation. Being increasingly popular in industrial applications, particular attention has been
devoted to induction motor drives. A good overview is provided
in [5]–[7], where the focus is on stator faults.
This paper focuses on stator electrical faults, specifically
open-phase or short-circuit faults, in the windings and on
low-cost noninvasive diagnostic systems for their detection. In
particular, electrical drive systems, which are highly integrated,
are considered here.
For such systems, a desirable low-cost diagnostic method
would allow the extraction of information about healthy, faulty,
Fig. 1.
Scheme of the system under consideration.
or incipiently faulty operating conditions from measurements
that are external to the box of the drive, particularly the
measurements that would be performed anyway for normal
operation reasons. Considering a drive that is supplied with
single-phase ac voltage, we focused on the analysis of the
current at the ac side of the drive. In previous papers, the authors
successfully analyzed the possibility of identifying dramatic
stator faults via wavelet transform of this current (see [8] and
[9]). In this paper, the focus is on the identification of incipient
faults on stator windings such as opening of a phase or short
circuit and the identification of wavelet domain features specific
for these types of faults. If a wavelet domain trend that is
connected to the fault worsening can be identified, then we can
think of exploiting this characteristic in the training of artificial
neural networks that categorize faults based on the wavelet
domain decomposition of current. The advantage we foresee
is the possible reduction of training patterns and network size.
The method pursued in this paper to identify the most significant and compact information about the faults contained
in the ac side current relies on the comparison between the
coefficients resulting from the wavelet decomposition of current
in healthy and faulty conditions. This approach aims to identify
the main fault information contribution among the different
time and scale (frequency) ranges.
II. P RINCIPLE OF THE P ROPOSED A PPROACH
Manuscript received June 10, 2004; revised September 6, 2006. This work
was supported in part by the Office of Naval Research under Grant N00014-021-0623 and Grant N00014-03-1-0434.
F. Ponci and A. Monti are with the Department of Electrical Engineering, University of South Carolina, Columbia, SC 292082-0133 USA (e-mail:
ponci@engr.sc.edu; monti@engr.sc.edu).
L. Cristaldi is with the Dipartimento di Elettrotecnica, Politecnico di Milano,
32-20133 Milan, Italy (e-mail: loredana.cristaldi@polimi.it).
M. Lazzaroni is with the Dipartimento di Tecnologie dell’Informazione,
Università degli Studi di Milano, 65-26013 Crema, Italy (e-mail: lazzaroni@
dti.unimi.it).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TIM.2007.907943
The target system of this paper is the induction motor drive.
This is a rather common element of complex systems and is
readily available for laboratory testing in a variety of operating
conditions. Having in mind the recent trend to more and more
integrated systems, where the drive can be considered as a
“black box,” we assume that the only accessible points of the
system are the ac input terminals (Fig. 1).
In what follows, the broader philosophy underlying this paper is presented. A variety of faults can occur within the considered drive, downstream from the ac side, such as bearings
0018-9456/$25.00 © 2007 IEEE
PONCI et al.: DIAGNOSTIC OF A FAULTY INDUCTION MOTOR DRIVE VIA WAVELET DECOMPOSITION
and bar faults, loose connections, winding shorts, and failure
of power switches, which are the most significant in terms of
frequency of occurrence, just to name a few. The study presented here is part of a research whose aim is, on one hand, to
identify effective methods for fault detection and, on the other
hand, to develop a faulty motor model for a complete and
multiphysics simulation environment that allows the simulation
of the motor drive, as part of a complex system, in a variety of
faulty conditions [10]. This knowledge may have consequences
on the design of a monitoring and diagnostic system in the presence of an induction motor drive and should allow for a tradeoff
between the complexity (and cost) of the monitoring system
and the capability to identify faults. Among the variety of faults
that may occur, the authors selected, for this introductory part
of the study, open-circuit and short-circuit faults in the stator
windings, which are represented as the variable stator phase
resistance.
The method used in this paper relies on the comparison
between the wavelet decomposition coefficients of the ac side
current. The comparison is performed between the wavelet
coefficients of the current in healthy conditions and in faulty
conditions for the identification of the fault-related information
carried by the wavelet coefficients. Wavelet decompositions
have the capability of identifying the signature of a signal. In
this particular case, a variation of resistance in one stator phase
is reflected in the current waveform at the ac side, which can
be detected through the wavelet decomposition of the current
when compared to a standard healthy case.
Preliminary results in case of total loss of one of the stator
or rotor phases are presented in literature [8]. The reported
results refer to a dramatic fault case. In this paper, the capability
of the presented approach to distinguish much lighter faults is
investigated. If the decomposition is able to point out the fault
information that is reflected at the ac side, then the wavelet
decomposition is also a convenient domain within which to
perform diagnostics tasks. Furthermore, for a fault identifier
based on a learning approach, the terms of the decomposition
that carry the most information are actually a conveniently
reduced set of data.
This paper shows how the information about faulty and
nonfaulty conditions can be studied in the wavelet domain,
provides a physical interpretation of the results, and shows
how the approach is weakly influenced by a change in load or
operating frequency and natural changes in the resistance value.
III. S IMULATION E NVIRONMENT
The role of simulation in the described study is two sided.
On one hand, the simulation environment is used to generate
sets of data for further analysis since in this early phase of
the work, the maximum possible flexibility in the choice of
operating conditions and parameter values is necessary and
is more difficult to guarantee with a physical experimental
setup. On the other hand, the results of this study can be
used to implement a drive model that is capable of compactly
representing variable fault conditions, without going into the
physics of the phenomenon, particularly if the purpose is to
investigate the impact on the rest of the system.
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For these purposes, the simulation environment that is
adopted for the study described above must be very flexible
and should allow various device models to coexist within
the same simulation and automatically on the run modification
of the simulation scheme. The virtual test bed (VTB) developed
over the past few years at the University of South Carolina,
has all the aforementioned characteristics. Its background and
underlying philosophy can be found in [10] and [11]. The VTB
schematic editor allows for a graphic-based programming of
the simulation environment. The VTB schematic of the drive
setup used in the simulation for this paper is reported in Fig. 2.
Variables can be virtually sampled at the desired sampling
frequency, whereas the simulation solver keeps using the simulation step that is more suitable for the kind of system under
analysis (Table I).
The model of the induction motor allows the modification of
the values of stator resistance for every phase. The simulations
were performed using the nominal value of Rs = 0.531 Ω, same
value for every phase, for the normal operating condition. Then,
the resistance of one phase of the stator was modified; the
values Rs = 0.7 Ω, Rs = 1 Ω, Rs = 4 Ω, and Rs = 8 Ω were assigned to simulate an approaching open-circuit phase, whereas
Rs = 0.1 Ω and Rs = 0.001 Ω were assigned to simulate an
increasing short-circuit phase. The system was simulated from
startup until steady state was reached and until ten steady-state
periods at 50 Hz were available. The steady-state part of the signal was then isolated by inspection and decomposed in wavelet
coefficients within a MATLAB environment. The system was
simulated in load conditions, at 65% of rated power, and in light
load conditions, at 30% of rated power, at 1200 and 600 r/min.
The fault considered here—a change in stator phase
resistance—is just one of the possible fault cases; nonetheless,
it constitutes a significant case study. A closed-form analysis
to study the effect of resistance change is extremely difficult, if
not impossible, for the described system.
The variable chosen for the analysis in this paper is the
current at the ac side of the drive. The reason for this choice
is that, if successfully implemented, such a diagnostic approach
could be cheaply introduced in drives that are not originally
equipped with separate diagnostic features.
IV. T HEORETICAL B ACKGROUND
As already mentioned in the previous paragraph, the main
goal is to study the possibility of a nonintrusive approach to
the diagnostic of the drive. In this perspective, the result of
this paper is not supposed to impact drive designers but plant
managers that are interested on added intelligent supervision
on a power system.
In this perspective, variable-speed drives represent a significant challenge for the following reasons.
• Variable-speed drives operate on a wide range of operating
frequencies, making traditional frequency analysis very
challenging if we do not assume to have access to direct
information from the control system.
• The input current waveform is highly distorted because of
the nonlinear interaction between the diode bridge and the
filter capacitor on the dc bus.
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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 56, NO. 6, DECEMBER 2007
Fig. 2. VTB schematic of the induction motor drive.
TABLE I
VALUES ADOPTED FOR THE ELEMENTS OF THE CIRCUIT IN FIG. 2
In this paper, we assume that no information is available from
the motor, the drive, or the control; in particular, the speed and
the voltage frequency are unknown. This assumption, which
is very reasonable from the plant manager perspective, makes
the process even more challenging. It is clear that because
of the limited information available, the diagnostic process
cannot be as effective as in the case of directly operating in
conjunction with the control system. Nevertheless, the authors
already demonstrated in previous publications that a signature
in the input current is present, and it can be used to detect
catastrophic faulty conditions [8], [9].
The theoretical question is how we can justify this signature.
Let us assume now that we perform the current sampling
synchronized with the input voltage; let us also assume that
the input voltage is lightly influenced by the current distortion
introduced by the drive operation.
These assumptions make sense considering that the passive
six-pulse front-end is typical of the small drives for which it
is reasonable to assume that the rated power is small compared
with the short-circuit power of the mains. A higher power driver
will present not only a more sophisticated front-end but also
more opportunity in terms of cost for a more sophisticated
diagnostic solution.
Let us also assume that we process, with the diagnostic system, a group of periods. The actual definition of the number of
periods can be considered to be an interesting design parameter.
The effects of the opening of a stator phase on the ac side
of the drive depend on the frequency at which the motor is
supplied and, inherently, the speed and characteristics of the
motor. Furthermore, the waveform change that results from
the opening or incipient opening of a stator phase affects the
commutation and the conduction period of the diodes. The
current is distorted in an unforeseeable way.
PONCI et al.: DIAGNOSTIC OF A FAULTY INDUCTION MOTOR DRIVE VIA WAVELET DECOMPOSITION
Fig. 3.
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Scaling function and wavelet function for Daubechies3 wavelet. In the decomposition and reconstruction filter, six coefficients are utilized.
In brief, we could say that the fault or incipient fault does
not significantly affect the shape of the current that is always
basically a pulse; however, the fault changes somehow the
current’s timing characteristics.
In this sense, a Fourier-based approach is not the natural
choice, whereas a time-frequency analysis enriches the amount
of information processed for the diagnostic.
The identification of the particular fault will still be investigated, and, in this respect, the authors believe that the effectiveness of the proposed approach cannot match that of custom
fault identification systems based on the extensive knowledge
of the system variables. However, the method here proposed is
able to trigger the alarming condition of incipient dangerous
conditions. As reported in [9], catastrophic conditions can be
immediately identified, whereas for the incipient fault, the
approach is mostly able to distinguish between a completely
healthy situation and an incoming problem. The real nature
of the problem might need further investigation; however,
this aspect, in the authors’ opinion, is not a major problem,
considering that we assume it to be in the early stage of the
problem.
In Section V, we will show how the approach effectively
works for a specific incipient fault case. In particular, we will
show how a time-frequency analysis based on wavelet transform combined with a correlation algorithm is able to detect a
signature in the input current.
V. W AVELET D OMAIN A NALYSIS
The ac side current signals of the actual system and of the
simulated system are compared based on the wavelet decomposition coefficients.
Wavelets are particular functions whose energy is concentrated both in time and frequency [12]–[14]. Speaking of the
wavelet basis, wavelets come in structured families, where
each member of the family allows the analysis of the signal
within a given range of frequencies and within a given interval
of time. The narrower wavelets of the family, for example,
allow a great time resolution analysis. The wavelet analysis
leads to the representation of the signal at a different detail
level; in fact, wavelets possess the multiresolution property.
Among the potentialities of this representation, a major one
is the possibility to independently choose the most convenient time-frequency resolution. The time resolution can be
different at different scales (frequencies; always in the limits of the Heisenberg principle). For example, a family can
be chosen so that the time resolution is particularly fine at
high frequencies, allowing the precise time identification of
glitches or sudden very high frequency contents. At the same
time, the frequency resolution could be particularly good at
lower frequencies, allowing the monitoring of the fundamental
component.
The approach proposed here involves the analysis of the signal through wavelet series decomposition. The decomposition
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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 56, NO. 6, DECEMBER 2007
Fig. 4. Scaling function and wavelet function for Daubechies4 wavelet. In the decomposition and reconstruction filter, eight coefficients are utilized.
results in a set of coefficients, each carrying local timefrequency information. An orthogonal basis function is chosen,
thus avoiding redundancy of information and allowing easy
computation.
The computation of the wavelet series coefficients can
be efficiently performed with the Mallat algorithm. In this
paper, Daubechies3 (six-coefficient filter) and Daubechies4
(eight-coefficient filter) wavelets are used. Fig. 3 depicts the
Daubechies3 with its scaling function and the related filter that
is useful for the application of the Mallat algorithm. Similarly,
Fig. 4 depicts the Daubechies4 with its scaling function and the
related filter.
The levels of resolution are limited by the number of samples. Considering that the samples themselves can be seen
as the highest possible resolution level, the decomposition is
performed from this level of detail up to coarser levels. With
ten levels of wavelets, for example, the decomposition results
in ten sets of wavelet coefficients plus the coefficients of the
scaling function.
The correlation between the wavelet coefficients at different
resolution levels was then computed for the purpose of investigating the common patterns of the ac side current in healthy
and faulty conditions. From this comparison, it is possible to
identify at what time-scale resolution level the main differences
between healthy and faulty conditions due to incipient faults are
more significant.
The following issues are then investigated.
1) Is it possible to detect a significant signature in a faulty
condition? This is a clear prerequisite for starting the next
step of the analysis, i.e., the incipient fault detection. This
statement was already proved in [9] but is repeated here
for the sake of completeness.
2) Which parameter can influence the signature? This is
significant to understand how many conditions we need to
perform a training of an intelligent system such as a neuro
identifier. In particular, we are interested on the influence
of loading conditions and operating output frequency.
Those two sets of parameters are able to describe the
complete operating area of the drive.
3) Can we detect any trend for an incipient fault? For this last
question, we focus on a single condition, i.e., increasing
the stator resistance. The idea is that we are not interested
in detecting a specific incipient fault but only in the
presence of a growing dangerous situation. In this respect,
we are interested in understanding whether a signature
is available and at which time resolution it would be
available.
The correlation between the currents in healthy and faulty
operating conditions in the time domain has been performed.
The results are posted in Fig. 5 for each of the fault conditions
considered later on.
PONCI et al.: DIAGNOSTIC OF A FAULTY INDUCTION MOTOR DRIVE VIA WAVELET DECOMPOSITION
Fig. 5.
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Correlation between current in time domain in different fault cases.
Fig. 7. Correlation between wavelet decomposition coefficients in healthy
conditions at 20 and 40 Hz.
Fig. 6. Correlation between wavelet decomposition coefficients in healthy
conditions in normal and light load operating conditions.
Fig. 5 shows that the correlation between signals is always
very close to 1; therefore, no significant difference can be
highlighted. The operation involves a considerable number of
samples (ten periods with 256 samples/period) and provides
little insight on the phenomenon.
The current signals are then decomposed in terms of wavelet
coefficients. The correlation between wavelet coefficients in
healthy operating conditions, but with different loading rates,
is performed. In Fig. 6, the results of this operation are reported
level by level or by decomposition detail. No major deviation
from unity occurs, highlighting that the different loading rates
do not produce a significant change in current waveform but
rather a rescaling. We interpret this result as the linearity of the
relationship between current waveforms in different operating
conditions within the rated performance of the drive. In Fig. 7,
the results of the correlation between wavelet coefficients at
different operating frequencies (speeds) are presented. In this
case, some differences can be noticed, particularly at the finer
resolution levels. As a consequence, it can be inferred that an
intelligent fault identifier based on wavelet coefficients needs
to receive training with data collected at a different operating
frequency.
Relying on the aforementioned results, a conclusion can be
drawn at this point. If the wavelet coefficients are to be used for
training a fault recognition system based on the correlation of
wavelet coefficients, then it is not necessary to feed the system
with data collected in a variety of different load operating
conditions, whereas training at different operating speed is
required. Notice that if a reliable validated model of the system
is available, a consistent part of the training can be performed
with data obtained in simulation.
The next focus is on whether it is possible to identify a
decomposition level that carries the most information about the
change in shape of the current waveform, i.e., about the fault.
For this purpose, the correlation between wavelet coefficients in
healthy and faulty operating conditions at the same speed and
load is performed.
In particular, the correlation between coefficients in healthy
conditions and in the variety of proposed faulty conditions is
computed at every decomposition level, and the results are presented in two forms: 1) Given a fixed decomposition level, the
correlation between healthy and faulty conditions is presented
for all examined faulty conditions; and 2) given a fixed fault, the
correlation between healthy and faulty conditions is presented
for all decomposition levels.
In Fig. 8, the results obtained at the finest resolution level (the
finest detail level in the wavelet analysis, corresponding to a
scale close to the sampling period) are presented. The deviation
of correlation coefficient from the unitary value indicates that
the presence of a fault introduces changes in the waveform at
this level.
These coefficient changes may represent minor variations in
the diode bridge on–off transition. In fact, the time-frequency
analysis allowed by the wavelet decomposition shows that the
magnitude of wavelet coefficients at the finest scale represented
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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 56, NO. 6, DECEMBER 2007
Fig. 8. Correlation between decomposition coefficients at level 1 in healthy
and all faulty conditions.
Fig. 9. Correlation between decomposition coefficients at all levels in healthy
and faulty conditions. Rs = 8 Ω.
against time presents an abrupt increase at the specific time instant when the ac side current reaches zero. This effect, though,
can be influenced by the presence of noise; therefore, it should
be treated with great caution. Furthermore, the attribution of
changes in the current waveform at this very fine resolution
level to the switching devices and their use for diagnostics purpose implies very reliable models of the switching devices. In
fact, the characteristics of the model of the switching devices determine their effect on the shape and entity of current
glitches.
In what follows, we will focus instead on the resolution level
that is related to power transfer; therefore, the focus will be on
coarser resolution level coefficients.
The first fault condition here considered in detail is the
resistance of stator phase, increasing its value up to 8 Ω from
Fig. 10. Correlation between decomposition coefficients at all levels in
healthy and faulty conditions. Rs = 5 Ω.
Fig. 11. Correlation between decomposition coefficients at all levels in
healthy and faulty conditions. Rs = 4 Ω.
the nominal value of 0.531 Ω. This change in resistance corresponds to a near opening of the phase. This dramatic fault case
is considered first since it may point out the most significant
characteristics within the present analysis. Subsequently, fault
cases with resistance equal to 5 and 4 Ω are considered.
Comparing Figs. 9–11, we notice that decomposition level 9
presents the most significant variations in all cases. As a consequence, further analysis focuses on this decomposition level.
In Fig. 12, the results representing the correlation between
decomposition coefficients at level 9 between healthy and
faulty conditions are reported. The analysis here is extended
to the short-circuit fault on the stator phase, represented as
a reduced phase resistance value. The correlation coefficients
between wavelet coefficients in healthy and faulty conditions
are reported for every considered fault case. Each fault case
PONCI et al.: DIAGNOSTIC OF A FAULTY INDUCTION MOTOR DRIVE VIA WAVELET DECOMPOSITION
Fig. 12. Correlation between decomposition coefficients at level 9 in healthy
and faulty conditions [fault level identified through the stator phase resistance
(in ohms)].
Fig. 13. Risk index and actual fault index from the fuzzy simulation after
400 epochs with the Gaussian membership function.
is identified by the corresponding stator phase resistance value
(in ohms).
Two things are to be noted. First, the correlation between
coefficients in healthy and faulty conditions is smaller for worse
fault conditions, and this occurs while approaching open-circuit
and short-circuit conditions. This criterion is a good indication
of the presence of a fault; other criteria, e.g., other coefficients,
may be more significant for the fault nature identification.
Second, barely any difference is visible for small variation of
resistance. Variations in the resistance value can be caused by
thermal changes that are part of the natural operation of the
machine. This characteristic prevents false alarms for minor
resistance variations. On the other hand, as a consequence,
precocious identification of resistance change that may result
in a fault is needed at this resolution level.
The number of wavelet coefficients at level 9 is much smaller
than the number of current samples originally collected. In this
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Fig. 14. Risk index and actual fault index from the fuzzy simulation after
800 epochs with the Gaussian membership function.
Fig. 15. Risk index and actual fault index from the fuzzy simulation after
400 epochs with the triangular membership function.
Fig. 16. Risk index and actual fault index from the fuzzy simulation after
800 epochs with the triangular membership function.
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TABLE II
EVALUATION OF THE MAXIMAL LOCAL ERROR
case, for example, given 2560 current samples, the coefficients
at level 9 are just nine.
The described approach allows, therefore, manipulation for
the diagnostic purpose or fault recognition training of far fewer
values than the total number of samples collected.
The reduction of the amount of data to be used for training
fault recognition systems, thanks to the compact data form provided by wavelets, is not the only peculiar characteristic of the
described approach. Consider, in fact, the physical interpretation of the findings presented here. The wavelet analysis with an
orthogonal wavelet basis has the capability to separate the major power transit contribution from the details of the waveform.
Furthermore, the time-scale decomposition of the details
of the waveform makes the approach more sensitive to local
waveform characteristics. Therefore, particularly in the presence of nonlinear components, local waveform changes can be
very significant, and time-local modification may be an early
symptom of fault occurrence.
VI. D ISCUSSION C ONCERNING
U NCERTAINTY E VALUATION
A preliminary analysis, supported by an analytical method
for the uncertainty evaluation when the signal analysis is
based on the discrete-time wavelet transform [16], about the
uncertainty contributions introduced by the measurement hardware and how the effect of the uncertainty sources propagates
through the wavelet algorithm, shows that these contributions
are, in the early stage, neglected if compared to the effect of the
neuro-fuzzy algorithm on the decision-making process.
In fact, the neuro-fuzzy algorithm has been realized with
the diagnostic purpose, and the ON/OFF state of the motor is
evaluated according to the value of the algorithm output.
To evaluate the effect of the algorithm parameter (i.e., number of epochs, membership function shape) on the output, a
preliminary test has been realized.
The employed pattern test is related to eight simulations
that were obtained by modifying load and frequency working
conditions; in this pattern, the fault conditions are obtained,
changing the stator phase resistance, with the aim to simulate an
approaching open-circuit phase and an increasing short-circuit
phase.
To synthesize and train the neural fuzzy network, the commercial tool AFM2.0 by ST Microelectronics has been used.
This software allows choosing between two different membership function shapes: Gaussian and triangular.
The comparison of Figs. 13 and 15, and of Figs. 14 and 16
shows how, with the same number of epochs, the triangular
membership function performs better than the Gaussian membership function.
Table II shows the maximal local error, which is evaluated as
the difference between the actual fault index value and the risk
index evaluated by the neuro-fuzzy algorithm.
VII. C ONCLUSION
The single-phase ac side current of an induction motor drive
was analyzed for the purpose of identifying the stator phase
electrical faults based on current signature. The fault was
modeled as a change in stator phase resistance. Open phase
and short-circuit phase cases were considered. Current data
were collected in a variety of operating conditions, combining
healthy and faulty conditions, load, and speed. Faulty cases,
in particular, were simulated with certain granularity in the
entity of the fault. A steady-state portion of the current was
decomposed in terms of wavelet coefficients; the variation of
wavelet coefficients in healthy and faulty conditions was examined. For this purpose, the correlation between the coefficients
was computed. A small value of the correlation coefficient is
considered as a symptom of a significant difference in pattern.
The analysis thus performed allowed the identification of specific resolution levels at which the wavelet coefficients carry
the most significant information about the operating conditions.
This analysis opens the way to simplified analysis for stator
phase fault identification. As a consequence, assuming that an
intelligent algorithm is trained for fault recognition, the choice
of training sets can be reduced to specific sets of wavelet
decomposition coefficients.
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Ferdinanda Ponci (S’01–M’02) received the M.S.
and Ph.D. degrees in electrical engineering from
the Politecnico di Milano, Milan, Italy, in 1998 and
2002, respectively.
In 2003, she joined the Department of Electrical Engineering, University of South Carolina,
Columbia, as an Assistant Professor with the Power
and Energy Research Group. Her current research is
in multiagent systems for control and monitoring and
in simulation of systems under uncertain conditions.
Antonello Monti (S’88–M’89–SM’02) received the
M.S. degree in electrical engineering and the Ph.D.
degree from the Politecnico di Milano, Milan, Italy,
in 1989 and 1994, respectively.
From 1990 to 1994, he was with the research
laboratory of Ansaldo Industria, Milan, where he
was responsible for the design of the digital control
of a large power cycloconverter drive. In 1995, he
joined the Department of Electrical Engineering,
Politecnico di Milano, as an Assistant Professor.
Since August 2000, he has been an Associate Professor with the Department of Electrical Engineering, University of South
Carolina, Columbia. He is the author or coauthor of more than 200 papers in
the field of power electronics and electrical drives.
Dr. Monti served as Chair of the IEEE Power Electronics Committee on
Simulation, Modeling, and Control, and as an Associate Editor of the IEEE
TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING.
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Loredana Cristaldi (S’91–M’01–SM’06) was born
in Catania, Italy, in 1965. She received the M.Sc.
degree from the University of Catania in 1992 and
the Ph.D. degree from the Politecnico di Milano,
Milan, Italy, in 1995, both in electrical engineering.
In 1999, she joined the Dipartimento di Elettrotecnica, Politecnico di Milano, as an Assistant Professor of electrical and electronic measurements. Since
2005, she has been an Associate Professor of electrical and electronic measurements with the Politecnico
di Milano. Her research interests include the field of
the measurements of electric quantities under nonsinusoidal conditions, virtual
instruments, and measurement methods for reliability, monitoring, and fault
diagnosis.
Dr. Cristaldi is a member of the Italian Association on Industrial Automation
(Anipla) and the Italian Informal Group on Electrical and Electronic Measurements of the National Research Council.
Massimo Lazzaroni (S’92–M’92) received the
M.Sc. degree in electronic engineering and the Ph.D.
degree in electrical engineering from the Politecnico di Milano, Milan, Italy, in 1993 and 1998,
respectively.
In 2001, he joined the Dipartimento di Elettrotecnica, Politecnico di Milano, as an Assistant Professor of electrical and electronic measurements. Since
December 2002, he has been an Associate Professor
of electrical and electronic measurements with the
Department of Information Technologies, Università
degli Studi di Milano Via Bramante, Crema, Italy. The scientific activity he
deals with includes digital signal processing techniques applied to electrical
measurements. In particular, his current research interests are concerned with
the application of digital methods to electrical measurements and measurements
on electric power systems in nonsinusoidal conditions and the use of soft computing in diagnostic and fault recognition in electrical systems. Furthermore, he
is also involved in research activities for the development of industrial sensors
and, finally, for quality control of industrial processes.
Dr. Lazzaroni is a member of the Comitato Elettrotecnico Italiano (CT66—
Safety of measuring, control, and laboratory equipment; CT85—Measuring
equipment for electrical and electromagnetic quantities; and CT500—
“Commissione mista UNI/CEI di Metrologia Generale”) and the Italian Informal Group on Electrical and Electronic Measurements of the National Research
Council.