Φ

Abstract – Development of portable devices for reliable

condition monitoring of induction machines has become the goal

of many researchers. In this context, the development of robust

algorithms for the automatic diagnosis of electromechanical

failures plays a crucial role. The conventional tool for the

diagnostic of most faults is based on the FFT of the steady-state

current. However, it implies significant drawbacks in industrial

applications in which the machine does not operate under ideal

stationary conditions (e.g. presence of pulsating load torques,

supply unbalances, noises…). In order to overcome some of

these problems, a novel transient-based methodology (Transient

Motor Current Signature Analysis, TMCSA) has been recently

proposed. The idea is to analyze the current demanded by the

machine under transient operation (e.g. during the startup) by

using proper Time Frequency Decomposition (TFD) tools in

order to identify the presence of specific patterns in the time-

frequency map caused by the characteristic evolutions of fault-

related components. However, despite the excellent results

hitherto obtained, the qualitative identification of the patterns

requires a certain user expertness, which implies difficulties for

the automation of the diagnosis. A new algorithm for the

automatic diagnostic of rotor bar failures is proposed in this

paper. It is based on the application of the Hilbert-Huang

Transform, sustained on the Empirical Mode Decomposition

process, for feature extraction, and the further application of

the Scale Transform (ST) for invariant feature selection. The

results prove the reliability of the algorithm and its generality to

automatically diagnose the fault in machines with rather

different sizes and load conditions.

Index Terms— AC Machine; Broken Rotor Bar; Condition

Monitoring; Diagnostics; Feature Extraction; Transient

Analysis; Hilbert-Huang Transform.

I. INTRODUCTION

HE development of algorithms for the automatic

diagnosis of different types of electromechanical failures

in induction machines has become the target of many recent

works. The ultimate objective is the implementation of these

algorithms in Digital Signal Processors (DSP’s) capable to

be incorporated in portable condition monitoring devices

This work was supported in part by ‘Ministerio de Educación’ within

the programs ‘Programa Nacional de Movilidad de Recursos Humanos del Plan Nacional de I+D+I 2008-2011’ and “Programa Nacional de proyectos de Investigación Fundamental”, project reference DPI2008-06583/DPI.

J. Antonino-Daviu M. Riera-Guasp and J. Roger-Folch are with Departmento de Ingeniería Eléctrica, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, SPAIN (e-mails: joanda@die.upv.es, mriera@die.upv.es, jroger@die.upv.es).

S. Aviyente and E. Strangas are with the Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA (e-mail: aviyente@egr.msu.edu, strangas@egr.msu.edu).

R.B. Pérez is with the Department of Nuclear Engineering, University of Tennessee, Knoxville, TN, USA (e-mail: rperez1@utk.edu).

able to diagnose the condition of the machine without

interfering with its normal operation. Most of the current

diagnostic devices are based on the application of the

traditional MSA method, consisting of applying the FFT to

the steady-state current and the subsequent detection of

specific fault-related frequencies in the spectrum. However,

as stated in many works, this approach may not work well in

many industrial applications in which the machine does not

work under pure stationary conditions. For instance, when

diagnosing rotor bar failures or mixed eccentricities, load

torque oscillations or supply unbalances often introduce

components similar to fault-related ones [1]. This can lead to

false positive diagnostics of the aforementioned failures.

Moreover, this conventional method is not suitable for the

detection of rotor breakages under unloaded conditions [2].

In order to avoid these drawbacks, recent works have

proposed alternative techniques, based on the analysis of the

currents demanded by the machine during transient operation

(Transient Motor Current Signature Analysis [2-6]). More

specifically, most of these methods are focused on the

analysis of the stator startup current. The main idea

underlying this alternative diagnosis methodology consists of

tracking the characteristic transient evolutions of fault-

related components, using them as indicators of the presence

of the failure. For this purpose, time-frequency

0 0.2 0.4 0.6 0.8 1 1.20

500

1000

1500

Time (s)

Spe

ed (r.p.

m.)

(a)

0 0.2 0.4 0.6 0.8 1 1.20

5

10

15

20

25

30

35

40

45

50

Time (s)

Fre

quen

cy (

Hz)

(b) Fig. 1. Direct on-line startup transient of an unloaded 1.1 kW IM with p=2:

(a) Evolution of the speed, (b) Evolution of the frequency of theLSH.

T

An EMD-based invariant feature extraction algorithm for rotor bar condition monitoring

Jose Antonino-Daviu, Selin Aviyente, Elias G. Strangas, Martin Riera-Guasp, Jose Roger-Folch and Rafael B. Pérez

978-1-4244-9303-6/11/$26.00 ©2011 IEEE

669

decomposition (TFD) tools specially suited for the analysis

of signals with a time-varying frequency spectrum must be

used. The mentioned characteristic evolutions are reflected

then through specific patterns appearing in the time-

frequency maps resulting from the application of these tools.

In this context, diagnosis techniques based on the application

of TFD tools such as wavelet transforms (Discrete Wavelet

Transform (DWT) [2-5], Undecimated Discrete Wavelet

Transform (UDWT) [7], Continuous Wavelet transform

(CWT) [6,8,9], Wavelet Packets (WP)…), Hilbert

Transform (HT) [10], Hilbert-Huang Transforms (HHT) [11-

13] or Wigner-Ville (WVD) and Choi-Williams

Distributions (CWD) [14-16] have been proposed in recent

years.

The main constraint of some of these approaches relies

on the fact that the detection of the failure is merely based on

the qualitative identification of the fault-related patterns.

This is, the user in charge of the diagnostic must be capable

to interpret and recognize the corresponding time-frequency

patterns associated to each failure when looking at the time-

frequency maps resulting from the application of the

corresponding TFD tool. For instance, in the case of broken

rotor bars, the user should recognize in the time-frequency

map the Λ-shaped pattern caused by the evolution of the

Lower Sideband Harmonic (LSH) during the transient [2,3].

This is the most relevant component associated with that

failure and its frequency (fLSH) is given by (1) (f=supply

frequency and s=slip) [2]. This pattern is depicted in Figure

1 (b) for a simulated startup of a 1,1 kW machine with 2 pole

pairs and supply frequency f=50 Hz [2-3]. The evolution of

the speed is shown in Fig. 1 (a).

(1)

This identification task is not always easy, mainly taking

into consideration that this pattern could be partially polluted

by components created by other faults (eccentricities) or

phenomena (position dependent load torque oscillations).

This necessity of certain user expertness for the identification

of fault-related patterns implies difficulties when automating

the fault diagnosis process, which is crucial for the

implementation of the diagnostic algorithms in portable

condition monitoring devices. Despite several Artificial

Intelligence (AI)-based techniques (namely, neural networks,

fuzzy logic, genetic algorithms and others) have been

developed for overcoming this problem [17], none of them

has hitherto been proved to be enough general and reliable

for being implemented in real devices able to diagnose the

presence of rotor bar failures. The goal of this paper is to propose a novel scale-

invariant algorithm for the automatic diagnosis of rotor bar

failures in induction motors. The algorithm requires a

previous application of a specific TFD tool, able to generate

an image representation of the time-frequency map in which

the fault-related components evolve. In this work, the

Hilbert-Huang Transform (HHT) is proposed for achieving

this purpose; this tool is based on the Empirical Mode

Decomposition (EMD) process and it has been applied with

success to the diagnosis of failures in nuclear reactors [18].

The HHT leads to clear time-frequency images in which the

Λ-shaped pattern caused by the transient evolution of the

Lower Sideband Harmonic (LSH) is easily detectable.

However, since the width of the Λ-pattern is dependent on

the startup length (which, at the same time, depends on

parameters such as machine size, load conditions, driven

inertia or supply voltage), it becomes necessary a further

normalization of the HHT-based images in order to obtain a

uniform pattern, in the case of fault, regardless of the startup

conditions or machine size. For this purpose, the Scale

Transform (ST) is applied. This transform, commonly used

in the image processing field, enables to obtain scale-

invariant feature vectors corresponding to different images

with a high reliability. Final computation of 2-D correlation

coefficients between scale invariant vectors resulting from

the ST enables to reach an automatic diagnosis conclusion.

The method is applied to a wide range of signals

corresponding to several machines with different sizes (from

few kW to hundreds of kW), constructive characteristics and

loading conditions. The results included in this paper prove

the reliability of the tool for the automatic diagnosis, even in

cases in which the traditional method could lead to erroneous

conclusions.

II. EMPIRICAL MODE DECOMPOSITION (EMD)

During these previous decades, several tools for analysis

of signals with a time-varying frequency spectrum have

proliferated. Application of each one of these tools provides

a particular time-frequency representation of the analyzed

signal in which the time-evolution of characteristic fault-

Fig. 2. Basic scheme of EMD algorithm.

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related frequency components can be tracked. Each TFD tool

provides particular advantages and drawbacks regarding the

tracking of fault-related components.

In this paper, the HHT is applied for the extraction of the

LSH caused by a broken bar. The main reason is that the

HHT is a cutting-edge tool enabling a clear extraction of the

transient evolution of low-frequency fault-related harmonics,

leading to clear image representations of the time-frequency

map with rather low computational requirements. Moreover,

the fault-related patterns are extracted in a twofold way;

through the characteristic waveforms raising in the intrinsic

mode functions (imf’s) resulting from the HHT or through

the 2-D time-frequency representations provided by the

Hilbert-Huang spectra of these imf’s [11].

The HHT was introduced by N.E. Huang [19-20] and it is

based on the decomposition of a signal in terms of empirical

modes and on their representation within the context of the

Complex Trace Method (CTM), formerly introduced by

Gabor [21]. In the case of multi-component, noise corrupted

signals the CTM fails because the Hilbert transform

processing of those noisy waves generates spurious

amplitudes at negative frequencies [18]. Huang [19-20]

developed a new signal analysis approach to avoid

generating unphysical results. In this approach, the Hilbert

transform is not directly applied to the signal itself but to

each of the members of an empirical decomposition of the

signal (named intrinsic mode functions (imf’s)).

The process to create the imf’s (known as “sifting”) is

simple [18]: provided a certain signal X(q) (q representing

either time or an spatial coordinate), a sequence of steps are

followed:

1. The local extrema of the data are identified and used to

create upper and lower envelopes which enclose the

signal completely.

2. From this envelope, a running mean is created.

3. By subtracting this “mean” from the data, one obtains a

new function, which must have the same number of zero

crossings and extrema (i.e. it exhibits symmetry across the

q-axis).

4. If the function so constructed does not satisfy this

criterion, then the “sifting” process continues until some

acceptable tolerance is reached [19].

5. The resulting q-series is the first ‘imf”, c1(q), and

contains the highest frequency oscillations found in the

data (the shortest time scales).

6. Imf1, is then subtracted from the original data, and this

difference is taken as if it were the original signal.

7. Then, the sifting process is applied again to the new

signal.

8. This procedure for finding modes, cj(q), continues until

the last mode, the residue Rn, is found. This will contain

the trend (i.e., the “time –varying” mean). Thus, the signal,

X(q), is given by the sum:

∑=

+=n

j

j RnqcqX1

)()( (2)

In [19], the completeness and orthogonality of the imf-

expansion are discussed in detail. Fig. 2 shows the basic

scheme of the EMD.

Further application of the Hilbert Transform (HT) to a given intrinsic mode function, imf, generates a q-frequency

plot, the Hilbert-Huang (HH) spectrum, H (ω, q), where ω is the instantaneous frequency [11]. On the other hand, the marginal spectrum can be also defined according to (3), where Q is the total data length [40];

qdqwHwhQ

∫ ⋅=

0),()( (3)

Whereas the HH spectrum offers a measure of the amplitude contribution for each frequency and time, the marginal spectrum (power spectral density) is a measure of the total amplitude (or energy) contribution from each frequency [22].

III. EMD-BASED PATTERN EXTRACTION

The HHT was applied to different startup current signals

corresponding to a 1,1 kW motor. The characteristics of the

tested motor are: Star connection, rated voltage (Un): 400V,

rated power (Pn): 1.1 kW, 2 pair of poles, primary rated

current (I1n): 2.7A, rated speed (nn): 1410 rpm and rated slip

(sn): 0.06. The number of rotor bars was 28. The motor was

directly coupled to a DC machine acting as a load.

Interchangeable rotors with different number of breakages

were considered. The breakages were forced in the

laboratory, drilling the holes in the selected bars.

Six transient data sets of induction machines stator current subject to different number of bar breakages and load conditions were analyzed (Table I). By applying the HHT, each data set was decomposed into two intrinsic mode functions (imf1 and imf2) and their respective HH spectra calculated. The reason for selecting two imf’s was to isolate all the frequency components below the fundamental frequency (i.e. the fault component fLSH) in one single imf (imf2). Then, imf1 would only reflect the evolution of the fundamental component.

TABLE I. ANALYZED DATA SETS

SET MACHINE CONDITION

q1 q2 q3 q4 q5 q6

two broken bars , unloaded two broken bars , loaded

one broken bar , unloaded one broken bar , loaded

healthy , unloaded healthy , loaded

The HH spectra of the second intrinsic modes (imf2) for the six data sets are shown in Fig. 3. Inspection of Fig. 3

reveals that the Λ-shaped pattern caused by the evolution of the LSH associated with the broken bar failure clearly appears in the faulty cases (Fig 3 (a) to (d)) whereas it is not present for healthy condition. The evolution of that pattern is quite characteristic; as the startup progresses, there is a first stretch in which the frequency decreases from f=50 Hz to 0 Hz and a second one in which the frequency increases again up to near 50 Hz. In addition, the color of the spectra informs

671

about the amplitude of the LSH at each time; higher amplitude is detected during the second stretch. This characteristic pattern found in the HHT current spectra of the faulty machines fits perfectly the theoretical waveform of the LSH [2,3], confirming that the HHT (and the EMD) is a suitable approach for diagnosing rotor asymmetries, since it enables a clear extraction and detection of the LSH produced by the fault [11]. As observed when comparing Fig. 3 (a) with Fig. 3 (b) and Fig. 3 (c) with Fig. 3 (d), although the pattern is present in all faulty cases, it has different width depending on the length of the startup (i.e. wider for the loaded IM and narrower for the unloaded). This makes difficult the direct comparison between images in order to determine if there is a fault. In order words, if the 2-D correlation coefficient is computed between images, a very low value would be obtained between unloaded and loaded cases even if both of them correspond to faulty condition. Therefore, it becomes necessary to standardize the patterns, this is, to obtain invariant features from two images differing in scale. For this purpose the scale transform will be used.

IV. INVARIANT FEATURES: THE SCALE

TRANSFORM

The Mellin transform (MT) of a certain function f(t)

represents that function in terms of scale. The scale can be

interpreted, as the frequency, as a physical characteristic of

the signal [23]. The MT of f(t) is defined as (4) [23, 24].

(4)

The scale transform (ST) is a particular case of the MT in

which s=-jc+1/2 with c∈ℜ. Therefore, the ST of f(t) can be

defined as [24, 25]:

(5)

Or, equivalently, it can be interpreted as the Fourier

transform of the function [24,25].

(6)

Note that the exponential sampling converts a scale factor

in the t variable onto a translation in scale, whereas the

Fourier transform in (6) converts the translation onto a phase

factor [24]. Therefore, given two signals f(t) and h(t)

normalized to the same energy and differing only in scale,

the magnitudes of their respective ST will be the same; their

ST will only differ in a phase factor [23, 24]. Hence:

(7)

This property of the ST is known as scale invariance and

enables the direct comparison between signals or functions in

which the scale is not consistent, provided that they have

similar energies.

Time (s)

Fre

quen

cy (

Hz)

g p

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

20

40

60

80

100

0.2 0.4 0.6 0.8 1 1.2 1.4

(a)

Time (s)

Fre

quen

cy (

Hz)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

20

40

60

80

100

0.2 0.4 0.6 0.8 1 1.2

(b)

Time (s)

Fre

quen

cy (

Hz)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

20

40

60

80

100

0.2 0.4 0.6 0.8 1 1.2 1.4

(c)

Time (s)

Fre

quen

cy (

Hz)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

20

40

60

80

100

0.5 1 1.5 2 2.5

(d)

∫∞

−

=0

)·ln·(

·)·(··2

1)( dtt

etfcD

tcj

f π

2

)()(t

t

k eeftf ⋅=

∫∞

−=0

·· ·)·(··2

1)( dtetfcD tcj

kf π

)()( cDcD hf =

∫∞

−=0

1·)·()( dtttfsF s

672

V. SCALE-INVARIANT FEATURE EXTRACTION

AND CORRELATION RESULTS

ST was applied to the HH spectra images resulting from

the application of the HHT in order to extract scale invariant

features. The ST enables a direct comparison between

images regardless of the length of the transient.

After this, the 2-D correlation coefficient was computed

between the resulting scale invariant feature matrices. The 2-

D correlation coefficient between 2 matrices A and B (with

the same size) is given by (8).

, (8)

where and are the means of the values in A

and B, respectively.

The computation of the 2-D correlation coefficient (ρ)

between two scale invariant feature matrices (resulting from

applying the ST to the 2-D images) corresponding to faulty

cases should lead to a value of ρ close to 1 (which indicates

maximum correlation). This should occur regardless of the

duration of the startup, since the application of the ST allows

discarding the effects of the scaling in the patterns (i.e. it

enables to obtain scale invariant features from the images).

On the other hand, application of the 2-D correlation

coefficient between faulty and healthy cases would lead to

lower values of ρ . Theoretically, these values should be very

low, since the Λ-shaped patterns do not appear in the healthy

machine. Nonetheless, by now, computation of ρ between

faulty and healthy matrices does not lead to so low values,

probably because a certain influence of the border effects

remains which increases the ‘similarity’ between faulty and

healthy-related matrices. Table II shows the results of

computing the 2-D correlation coefficient between the scale-

invariant feature matrices, corresponding to different faulty

and healthy cases for the 1,1 kW motor directly coupled to

the DC load (machine 1). These matrices result from

applying the ST to the HH spectra images described in the

section III. In the table, ‘BB’ denotes ‘broken bar’, u is

‘unloaded’ and ‘l’ loaded. The healthy cases are colored in

red.

TABLE II 2-D CORRELATION COEFFICIENTS BETWEEN INVARIANT FEATURE MATRICES

Table II shows the high correlation between faulty cases,

regardless of the load condition. Moreover, the correlation is

higher when the degree of failure is similar (highest values

between 2BB-l and 2BB-u and between 1BB-l and 1BB-u).

On the other hand, the correlation with respect the healthy

condition leads to much lower values.

Several additional tests were performed by coupling the

1,1 kW IM to another load by using a different coupling

system (based on pulleys and straps) (machine 2). The idea

was to analyze the correlation between the scale invariant

features in this case and those obtained for the previous one

in order to see if the new operation conditions of the machine

influence the robustness of the diagnostic algorithm. Table

III shows the correlation coefficients obtained when

comparing different faulty conditions for both machines.

High correlation was again observed between faulty cases

whereas low between faulty and healthy ones, including a

case in which the machine was healthy but supplied through

a fluctuating voltage (Heal_vble_U), a fact which could

make the diagnosis difficult.

TABLE III 2-D CORRELATION COEFFICIENTS BETWEEN MACHINES 1 AND 2

Machine1 → 2BB-u 2BB-l 1BB-u 1BB-l healthy-u

Machine 2

↓2BB-l 0.9244 0.9351 0.9012 0.9044 0.6223

Time (s)

Fre

quen

cy (

Hz)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

20

40

60

80

100

0.5 1 1.5 2 2.5 3

(e)

Time (s)

Fre

quen

cy (

Hz)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

20

40

60

80

100

0.2 0.4 0.6 0.8 1 1.2

(f) Fig. 3. HH spectrum resulting from the application of the HHT to the six

data sets: (a) q1, (b) q2, (c) q3, (d) q4, (e) q5 and (f) q6

2BB-u 2BB-l 1BB-u 1BB-l healthy-u

2BB-u 1 0.9455 0.9311 0.9101 0.5776

2BB-l 1 0.9422 0.9445 0.5802

1BB-u 1 0.9512 0.6001

1BB-l 1 0.5599

healthy-u 1

∑∑ ∑∑

∑∑

−−

−−

=

m n m n

mnmn

m n

mnmn

BBAA

BBAA

))()·()((

))·((

22ρ

A B

673

2BB-u 0.9340 0.9301 0.9288 0.9112 0.6004

Heal_vble_U 0.6555 0.6677 0.6211 0.6445 0.8976

Heal-l 0.5889 0.5777 0.6204 0.6011 0.9020

Finally, in order to prove the generality of the method, 2-D

correlation coefficients were computed between the feature

matrices for the machine 1 cases and those obtained with

large size motors operating in mining installations and whose

faulty conditions were known. These data were kindly

provided by [26]. Table IV shows these results, also very

satisfactory (highest correlations between similar faulty

conditions). This table is very illustrative since it shows how,

given a certain motor with an initially unknown condition (let

us say the 320 kW motor in Table IV), direct correlation

between the feature matrices for that motor and the feature

matrices corresponding to different healthy and faulty

conditions of a reference motor (let us say the 1,1 kW motor

(machine 1) of Table IV would automatically indicate if the

diagnosed machine is healthy or faulty (according to Table

IV the 320 kW motor will have broken rotor bars, since the

correlation coefficients in faulty cases are much higher. This

was confirmed after direct observation). This same method

could be applied to diagnose the condition of any other

machine regardless its size. This is due to the potential

provided by the Scale Transform (ST).

TABLE IV 2-D CORR. COEFF. BETWEEN MACHINE 1 AND LARGE MOTORS

Machine1 → 2BB-u 2BB-l 1BB-u 1BB-l healthy-u

320kW motor with BB

0.8499 0.8467 0.8542 0.8676 0.5454

850/450 kW motor with BB

0.8233 0.8455 0.8467 0.8562 0.5162

850/450 kW healthy motor

0.4642 0.4145 0.4954 0.4877 0.9087

VI. CONCLUSION

This work proposes a new algorithm for the automatic

diagnostic or rotor bar failures in IM. This approach is based

on the extraction of fault-related patterns from the original

startup current signals by applying the HHT. These

characteristic patterns are represented as images when

considering the HH spectrum of the imf’s resulting from the

HHT. Subsequent application of the scale transform enables

to eliminate the influence of the loading conditions or of the

machine size on the length of the resulting patterns, taking

advantage of the scale invariant nature of this transform.

Finally, computation of the 2-D correlation coefficient with

respect a reference Λ-pattern enables to diagnose the

condition of the machine.

The developed algorithm enables, known the patterns

corresponding to different faulty cases of a machine

considered as a reference, to automatically find out the

condition of any machine regardless of its size or operating

conditions.

Unlike other methods, this technique does not require

speed measurement. A single phase current, which can be

measured in a non-invasive way, is enough for its

application. The results obtained with machines from few

kW up to hundreds of kW confirm the validity and generality

of the approach. At this stage the algorithm has been applied

to detect the presence or absence of the failure. Further

research will enable to specify the exact degree of failure in

the machine (2, 3, 4 bars…) based on the energy of imfs.

VII. REFERENCES

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[2] J. Antonino-Daviu, M. Riera-Guasp, J. Roger-Folch and M.P. Molina, “Validation of a New Method for the Diagnosis of Rotor bar Failures via Wavelet Transformation in Industrial Induction Machines,” IEEE Trans. on Ind. Appl., vol. 42, no. 4, Jul/Aug 2006, pp. 990-996.

[3] M. Riera-Guasp, J. A. Antonino-Daviu, M. Pineda-Sanchez, R. Puche-Panadero, J. Perez, "A General Approach for the Transient Detection of Slip-Dependent Fault Components Based on the Discrete Wavelet Transform." IEEE Trans. Ind. Elec., vol. 55, 2008, pp. 4167-4180.

[4] S.H. Kia, H. Henao and G.A. Capolino, “Diagnosis of Broken-Bar Fault in Induction Machines Using Discrete Wavelet Transform Without Slip Estimation,” IEEE Trans. on Ind. Appl., Vol. 45, No. 4, July/August 2009, pp. 1395-1404.

[5] A.Ordaz-Moreno, R.Romero-Troncoso, J.A.Vite-frías, J.Riviera- Gillen, A.García-Pérez, “Automatic online diagnostic algorithm for broken-bar detection on induction motors based on Discrete Wavelet Transform for FPGA implementation”, IEEE Transactions on Industrial Electronics, , vol.55, no.5, pp.2193-2200, May. 2008.

[6] J. Cusido, L. Romeral, J.A. Ortega, J.A. Rosero, A. Garcia Espinosa, "Fault Detection in Induction Machines Using Power Spectral Density in Wavelet Decomposition," IEEE Transactions on Industrial Electronics, vol.55, no.2, pp.633-643, Feb. 2008

[7] W. G. Zanardelli, E. G. Strangas, and S. Aviyente, "Identification of Intermittent Electrical and Mechanical Faults in Permanent-Magnet AC Drives Based on Time-Frequency Analysis," IEEE Trans. Ind. Appl., vol. 43, pp. 971-980, 2007.

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VIII. BIOGRAPHIES

Jose Antonino-Daviu (S’04/M’08) received his M.S. and Ph. D. degrees in Electrical Engineering, both from the Universitat Politècnica de València, in 2000 and 2006, respectively. He was working for IBM during 2 years, being involved in several international projects. Currently, he is Associate Professor in the Department of Electrical Engineering of the mentioned University, where he develops his docent and research work. He has been invited professor in Helsinki University of Technology (Finland) in 2005 and 2007 and in Michigan State University (USA) in 2010. He has over 60 publications between international journals, conferences and books. His primary research interests are condition monitoring of electric machines, wavelet theory and its application to fault diagnosis and design and optimization of electrical installations and systems.

Selin Aviyente received her B.S. degree with high honors in electrical and electronics engineering from Bogazici University, Istanbul in 1997. She received her M.S. and Ph.D. degrees, both in Electrical Engineering: Systems, from the University of Michigan, Ann Arbor, in 1999 and 2002, respectively. In August 2002, she joined the Department of Electrical and Computer Engineering at Michigan State University where she is currently an associate professor. Her research focuses on the theory and applications of statistical signal processing, in particular non-stationary signal analysis. She is interested in developing methods for efficient signal representation,

detection and classification. She has published over 80 refereed journal articles and conference proceedings on time-frequency analysis, signal detection and classification. She is the recipient of 2005 Withrow Teaching Excellence Award and 2008 NSF CAREER Award.

Elias Strangas received the Dipl. Eng. degree in electrical engineering from the National Technical University of Greece, Athens, Greece, in 1975 and the Ph.D. degree from the University of Pittsburgh, Pittsburgh, PA, in 1980. He was with Schneider Electric (ELVIM), Athens, from 1981 to 1983 and the University of Missouri, Rolla, from 1983 to 1986. Since 1986, he has been with the Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, where he heads the Electrical Machines and Drives Laboratory. His research interests include the design and control of electrical machines and drives, finite-element methods for electromagnetics, and fault prognosis and mitigation of electrical drive systems.

M. Riera-Guasp (M’95) received his M.Sc. degree in industrial engineering and his Ph.D. degree in electrical engineering from the Universitat Politècnica de València (Spain) in 1981 and 1987, respectively. Currently he is an associate professor in the Department of Electrical Engineering of the Universidad Politécnica de Valencia. His research interests include condition monitoring of electrical machines, applications of the wavelet theory to electrical engineering, and efficiency in electric power applications.

Jose Roger-Folch obtained his M.Sc. degree in Electrical Engineering in 1970 from the Polytechnic University of Cataluña and his Ph.D in 1980 from the Universitat Politècnica de València, Spain. From 1971 to 1978 he worked in the Electrical Industry as Project Engineer. Since 1978, he joined the Universitat Politècnica de València and he is currently Professor of Electrical Installations and Machines. His main research areas are the Numerical Methods (F.E.M. and others) applied to the Design and Maintenance of Electrical Machines and Equipments.

Rafael B. Pérez is Master in Chemistry from University of Valencia (1951), Master in Nuclear Engineering from MIT (Massachusets Institute of Technology) (1958) and PhD in Physics from University of Madrid (1961). He has worked for: Spanish Air Force Meteorology Officer (1948-1955); Junta de Energia Nuclear (Spain) (1955-1961); Associate Professor of Nuclear Engineering University of Florida (1961-1967); Senior Research Scientist Oak Ridge National Laboratory (ORNL) (1967-1980), Full Professor of Nuclear Engineering University of Tennessee (UTK) (1980 - 1997). Presently, he is Emeritus Professor of UTK He has over 200 reviewed papers on signal analysis, nuclear reactor dynamics, quantum mechanics and neutron cross section theory. He is Fellow of the American Nuclear Society (ANS); in 1999 he was awarded with the ANS Wigner Prize.

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