Condition Monitoring and Fault Diagnosis of Electrical ... - IEEE Xplore

IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 20, NO. 4, DECEMBER 2005

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Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review Subhasis Nandi, Member, IEEE, Hamid A. Toliyat, Senior Member, IEEE, and Xiaodong Li, Student Member, IEEE

Abstract—Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. The manufacturers and users of these drives are now keen to include diagnostic features in the software to improve salability and reliability. Apart from locating specific harmonic components in the line current (popularly known as motor current signature analysis), other signals, such as speed, torque, noise, vibration etc., are also explored for their frequency contents. Sometimes, altogether different techniques, such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. In addition, human involvement in the actual fault detection decision making is slowly being replaced by automated tools, such as expert systems, neural networks, fuzzy-logic-based systems; to name a few. It is indeed evident that this area is vast in scope. Hence, keeping in mind the need for future research, a review paper describing different types of faults and the signatures they generate and their diagnostics’ schemes will not be entirely out of place. In particular, such a review helps to avoid repetition of past work and gives a bird’s eye view to a new researcher in this area. Index Terms—Condition monitoring, electrical motors, fault diagnosis, review.

I. INTRODUCTION

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HE HISTORY of fault diagnosis and protection is as archaic as the machines themselves. The manufacturers and users of electrical machines initially relied on simple protection such as overcurrent, overvoltage, earth-fault, etc. to ensure safe and reliable operation. However, as the tasks performed by these machine grew increasingly complex, improvements were also sought in the field of fault diagnosis. It has now become very important to diagnose faults at their very inception; as unscheduled machine downtime can upset deadlines and cause heavy financial losses. The major faults of electrical machines can broadly be classified as the following [1]: 1) stator faults resulting in the opening or shorting of one or more of a stator phase winding; 2) abnormal connection of the stator windings; 3) broken rotor bar or cracked rotor end-rings;

Manuscript received March 1, 2004; revised June 1, 2004. This work was supported in part by the Texas Advanced Research Program under Grant 95-P083, in part by the Department of Energy under Grant DE-FG07-98ID13641, in part by the University of Victoria, in part by the Canadian Foundation For Innovation’s New Opportunity Funds, British Columbia Knowledge Development Funds, and Natural Sciences and Engineering Research Council of Canada’s Discovery Grant. Paper no. TEC-00055-2004. S. Nandi and X. Li are with the Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 3P6, Canada (e-mail: [email protected]; [email protected]). H. A. Toliyat is with the Department of Electrical Engineering, Texas A&M University, College Station, TX 77840 USA (e-mail: [email protected]). Digital Object Identifier 10.1109/TEC.2005.847955

4) static and/or dynamic air-gap irregularities; 5) bent shaft (akin to dynamic eccentricity) which can result in a rub between the rotor and stator, causing serious damage to stator core and windings; 6) shorted rotor field winding; 7) bearing and gearbox failures. Of the above types of faults: 1) bearing; 2) the stator or armature faults; 3) the broken rotor bar and end ring faults of induction machines; and 4) the eccentricity-related faults are the most prevalent ones and, thus, demand special attention. These faults produce one or more of the symptoms as follows: 1) unbalanced air-gap voltages and line currents; 2) increased torque pulsations; 3) decreased average torque; d) increased losses and reduction in efficiency; 5) excessive heating. For the purpose of detecting such fault-related signals, many diagnostic methods have been developed so far. These methods to identify the above faults may involve several different types of fields of science and technology. They can be described as follows [1], [2]: 1) electromagnetic field monitoring, search coils, coils wound around motor shafts (axial flux-related detection); 2) temperature measurements; 3) infrared recognition; 4) radio-frequency (RF) emissions monitoring; 5) noise and vibration monitoring; 6) chemical analysis; 7) acoustic noise measurements; 8) motor-current signature analysis (MCSA); 9) model, artificial intelligence, and neural-network-based techniques. Section II will deal with the common faults and their diagnosis techniques. A brief introduction to fault detection using artificial-intelligence (AI) techniques has been included in Section III. II. VARIOUS TYPES OF FAULTS AND THEIR DETECTION TECHNIQUES A. Bearing Faults The majority of the electrical machines use ball or rolling element bearings. Each bearing consists of two rings—one inner and the other outer. A set of balls or rolling elements placed in raceways rotates inside these rings [2]. Even under normal operating conditions with balanced load and good alignment, fatigue failures may take place. These faults may lead to increased vibration and noise levels. Flaking or spalling of bearings might

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occur when fatigue causes small pieces to break loose from the bearing. Other than the normal internal operating stresses caused by vibration, inherent eccentricity, and bearing currents [3] due to solid state drives, bearings can be spoiled by many other external causes such as the following: 1) contamination and corrosion caused by pitting and sanding action of hard and abrasive minute particles or corrosive action of water, acid, etc. 2) improper lubrication; which includes both over and under lubrication causing heating and abrasion; 3) improper installation of bearing; by improperly forcing the bearing onto the shaft or in the housing (due to misalignment), indentations are formed in the raceways (brinelling). Almost 40%–50% of all motor failures are bearing related. Sometimes bearing faults might manifest themselves as rotor asymmetry faults [2], which are usually covered under the category of eccentricity-related faults. Otherwise, the ball bearingrelated defects can be categorized as [1] outer bearing race defect, inner bearing race defect, ball defect, and train defect. The vibration frequencies to detect these faults are given by

for an outer bearing race defect for an inner bearing race defect for a ball defect for a train defect

(1)

where is the rotational frequency, is the number of balls, and are the ball diameter and ball pitch diameter, respectively, and is the contact angle of the ball (with the races). Schoen et al. [4] have shown that these vibration frequencies reflect themselves in the current spectrum as (2) , and are one of the characteristic where vibration frequencies. However, the experimental results were presented for rather extensive bearing damage (such as a hole in the outer race of the bearing; brinelling induced by a vibration table). The implementation of an unsupervised on- line detection of these faults using artificial neural networks (ANNs) has also been described in [5]. Yazici et al. [6] have reported an adaptive, statistical time frequency method for the detection of bearing faults. Experiments were conducted on defective bearings with scratches on the outer races and bearing balls and cage defects. It has been claimed that all defective measurements were correctly classified as defective. However, the detection procedure required extensive training for feature extraction. Detection of bearing faults using vibration signals is affected by machine speed [7], particularly when the bearing condition is deteriorating. The machine vibration may decrease even though failure is imminent. The bearing life is also influenced by variable machine

speed. The advantage of envelope detection techniques over traditional spectrum analysis is demonstrated in detecting these faults [8]. Reference [8] also presents the fundamentals of bearing fault detection techniques in a very simplified manner. B. Stator or Armature Faults These faults are usually related to insulation failure. In common parlance, they are generally known as phase-to-ground or phase-to-phase faults. It is believed that these faults start as undetected turn-to-turn faults that finally grow and culminate into major ones [9]. Almost 30%–40% of all reported induction motor failures fall into this category [9]. Armature or stator insulation can fail due to several reasons. Primary among these are [10]: 1) high stator core or winding temperatures; 2) slack core lamination, slot wedges, and joints; 3) loose bracing for end winding; 4) contamination due to oil, moisture, and dirt; 5) short circuit or starting stresses; 6) electrical discharges; 7) leakage in cooling systems. There are a number of techniques to detect these faults. For large generator and motor stator windings rated 4 kV and above, online partial-discharge (PD) test methods give very reliable results [11]. Even a portable test instrument called TGA-B is available for this purpose. However, for low-voltage motors, stator fault detection procedures are yet to be standardized. Penman et al. [12] were able to detect turn-to-turn faults by analyzing the axial flux component of the machine using a large coil wound concentrically around the shaft of the machine. Even the fault position could be detected by mounting four coils symmetrically in the four quadrants of the motor at a radius of about half the distance from the shaft to the stator end winding. The frequency components to detect in the axial flux component are given by (3) where

is the number of pole pairs, is the mains frequency, and and is the slip. The axial flux-based detection technique works well even in the presence of supply harmonics as in the case with VSI-driven induction motors [13]. Toliyat and Lipo [14] have shown through both modeling and experimentation that these faults result in asymmetry in the machine impedance causing the machine to draw unbalanced phase currents. This is the result of negative-sequence currents flowing in the line as also have been shown in [15] and [16]. However, negative-sequence currents can also be caused by voltage unbalance, machine saturation, etc. Kliman et al. [9] model these unbalances which also includes instrument asymmetries. It is reported that with these modifications, it is possible even to detect a one turn “bolted” fault out of a total 648 turns. A similar technique has been used in [17] with a power decomposition technique (PDT) to reduce harmonic effects and negative-sequence reactance to reduce temperature and slip variation effects on negative-sequence current measurement. The difference between the positive sequence of current under the faulty

NANDI et al.: CONDITION MONITORING AND FAULT DIAGNOSIS OF ELECTRICAL MOTORS—A REVIEW

and the healthy conditions divided by the positive-sequence current under the healthy conditions is also reported to an effective diagnostic index [18]. Statistical process control (SPC) techniques have also been applied to detect stator faults [19]. A model to estimate and detect stator turn–turn short-circuit faults in time domain has been reported in [20]. In [21], a fuzzy fault detector using Concordia patterns to detect stator unbalance and open-circuit faults has been developed. The patterns were derived from the current Concordia vector based on a three-phase to two-phase – transformation of line current in stationary coordinates. A few MCSA-based techniques for interturn stator fault detection have been reported [22], [23]. Both low- and high-frequency components, almost similar to those observed with eccentricity-related faults, are shown to be present. However, the physics behind the existence of such components are not clearly explained. Also, issues such as voltage unbalance, constructional imperfections that produce similar effects, are not addressed. Stator fault detection using external signal injection is discussed in [24]. Angular fluctuation of the stator current space vector [25] has been monitored in detecting stator interturn faults, rotor faults. The phase-angle variations are analyzed in frequency domain. The Goertzel algorithm is used for real-time implementation. A strong third-harmonic component can also be found in line current with stator interturn faults [26]. Monitoring the change in positive-sequence current using the multiple reference frame theory was additionally suggested in [26] and [27] for detection. Nandi and Toliyat [28] have observed that the shorted stator turns act as a search coil to pick up rotor magnetomotive-force (mmf) harmonics in a squirrel cage machine given by No. of rotor bars (4) Although MCSA can detect these components, they may be confused with voltage unbalance in some machines. Fortunately, they can be unambiguously detected at the terminal voltages of the machine just after switching it off. The experimental results are shown in Fig. 1. In a healthy machine, the pole pair number associated with this time particular harmonic does not match that of a symmetrical three-phase winding. Hence, it is not detectable. Detection of stator voltage unbalances and single phasing effects using traditional and advanced signal-processing techniques have been described in [29] and [30]. C. Broken Rotor Bar and End-Ring Faults Unlike stator design, cage rotor design and manufacturing has undergone little change over the years. As a result, rotor failures now account for around 5%–10% of total induction motor failures (Bonnett and Soukup [31], Kliman et al. [9], [32]). Cage rotors are of two types: cast and fabricated. Previously, cast rotors were only used in small machines. However, with the advent of cast ducted rotors, casting technology can be used even for the rotors of machines in the range of 3000 kW. Fabricated rotors are generally found in larger or special application machines. Cast rotors, though more rugged than the fabricated

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Fig. 1. Experimental plots of normalized line voltage spectra of a 44-rotor bar, four-pole motor under healthy (top), unbalanced supply voltage (–5%, –2.5%) (middle) and stator interturn short (5/324) (bottom). The 21st harmonic increases by almost 10 dB only with stator fault.

type, can almost never be repaired once faults such as cracked or broken rotor bars develop in them. The reasons for rotor bar and end-ring breakage are several. They can be caused by the following: 1) thermal stresses due to thermal overload and unbalance, hot spots, or excessive losses, sparking (mainly fabricated rotors); 2) magnetic stresses caused by electromagnetic forces, unbalanced magnetic pull, electromagnetic noise, and vibration; 3) residual stresses due to manufacturing problems; 4) dynamic stresses arising from shaft torques, centrifugal forces, and cyclic stresses; 5) environmental stresses caused by for example contamination and abrasion of rotor material due to chemicals or moisture; 6) mechanical stresses due to loose laminations, fatigued parts, bearing failure, etc. Kliman et al. [32], Thomson and Stewart [33], Filippetti et al. [34], and Elkasabgy et al. [35] used spectrum analysis of machine line current (MCSA) to detect broken bar faults. They investigated the sideband components around the fundamental for detecting broken bar faults (5) while the lower sideband is specifically due to a broken bar, the upper sideband is due to consequent speed oscillation. In fact,

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[34] shows that broken bars actually give rise to a sequence of such sidebands given by (6) The motor-load inertia also affects the magnitude of these sidebands. Other spectral components that can be observed in the stator line current are given by Kliman et al. [32] and Gaydon [36] (7) are detectable broken bar frequencies; . Elkasabgy et al. [35] has also shown that broken bar faults can also be detected by time- and frequency-domain analysis of induced voltages in search coils placed internally around stator tooth tip and yoke and externally on the motor frame. . The frequency components are given by (6) with and frequency Torque and speed signals also contain components with broken rotor bars [34], [35]. Following the works of Penman [37], detection of these faults is also possible by frequency-domain analysis of shaft flux or more generally axial leakage flux which is monitored by using an external search coil wound around the shaft of a machine. The fre. The quency components are still given by (6) with modeling of rotor bar and end-ring faults has been described in [14]. Broken bar detection using state and parameter estimation techniques have also been reported [38]. However, the current spectrum and the parameter estimation approach has been compared and the former has been found to be more efficient [39]. Masoud and Toliyat [40] proposed using pattern recognition to detect broken rotor bars. The rotor speed is estimated from stator current and then the featured vector is extracted as an input to Baye’s classifier. The time-stepping coupled Finite element-state space (TSCFE-SS) method has been used [41] to compute core losses and copper losses with broken-bar faults in variable speed drives. Time-series data mining (TSDM) in conjunction with the TSCFE-SS method has been used to extract broken bar information from torque data [42], [43]. Interestingly, while literature abounds with MCSA-based fault detection, it is shown in [44] that spectral components related to broken bar faults are stronger in per-phase partial power and total power than in stator line currents. The best result is obtained with partial power. As suggested in [45], interbar currents are present in uninsulated rotor cages, where the contact between the rotor core and the bars are good. Interbar currents reduce [46] the magnetic imbalance caused by broken bars. This makes detection of broken bars more difficult, particularly at early stages. Significant interbar current is present with broken bars even in double-cage induction motors [47]. Axial vibration spectra can also be used to diagnose rotor bar faults [48]. The axial vibration arises out of the axial force generated by the interaction of the interbar current and stator flux. Experimental results have been given for copper cage rotors with one broken bar. Figs. 2 and 3 show simulated current, speed, and torque waveforms and their related spectra with two partially broken bars and two partially broken end-ring faults for a 3-ph 3-hp 60-Hz four-pole skewed 44-rotor bar induction motor. The where

Fig. 2. Simulated plots of normalized line current spectra around fundamental and fifth and seventh time harmonic (top row), torque, and its spectra (middle row), speed, and its spectra (bottom row) with two bars partially broken. Slip = 0:033. PSD is the acronym for power spectral density.

Fig. 3. Simulated plots of line current and speed (top row) and their normalized spectra (bottom row) for the two end rings partially broken. Slip = 0:036.

current components given by (4–6) and the 2 and 4 speed-related components can be clearly seen in the plots. However, in practice, the current sidebands around the fundamental may exist even when the machine is healthy, as can be seen in Fig. 4. This could be due to uneven rotor bar resistance because of the die-casting process, rotor asymmetry, etc. Also, components given by (6) may not show any marked change (Fig. 5). Hence, at least for small motors, it may be worthwhile to confirm the presence of broken bars through the speed spectra (Fig. 6).


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Fig. 6. Experimental plots of normalized speed spectra of healthy machine (top) and with four bars broken (bottom). Slip = 0:033.

The harmonics of the stator terminal voltages just after switching the motor off can also be used as a diagnostic measure [49]. Other transient detection techniques have been described in [50]. D. Eccentricity-Related Faults

Fig. 4. Experimental plots of normalized line current spectra of healthy machine (top) and with one to four bars partially broken (next four plots). Slip = 0:033. These plots have been obtained from a machine that is similar to the one simulated. Faults were introduced by drilling the bars.

Fig. 5. Experimental plots of normalized line current spectra of healthy machine (top) and with four bars broken (bottom) around the 5th and 7th time harmonics. Slip = 0:033.

Machine eccentricity is the condition of unequal air gap that exists between the stator and rotor (Vas [1], Heller and Hamata [51], Cameron et al. [52]). When eccentricity becomes large, the resulting unbalanced radial forces (also known as unbalanced magnetic pull or UMP) can cause stator to rotor rub, and this can result in damage of the stator and rotor. There are two types of air-gap eccentricity: the static air-gap eccentricity and the dynamic air gap eccentricity. In the case of the static air-gap eccentricity, the position of the minimal radial air-gap length is fixed in space. Static eccentricity may be caused by the ovality of the stator core or by the incorrect positioning of the rotor or stator at the commissioning stage. If the rotor-shaft assembly is sufficiently stiff, the level of static eccentricity does not change. In case of dynamic eccentricity, the center of the rotor is not at the center of the rotation and the position of minimum air-gap rotates with the rotor. This misalignment may be caused due to several factors such as a bent rotor shaft, bearing wear or misalignment, mechanical resonance at critical speed, etc. Dynamic eccentricity in a new machine is controlled by the total indicated reading (TIR) or “run-out” of the rotor (Barbour and Thomson [53]). An air-gap eccentricity of up to 10% is permissible. However, manufacturers normally keep the total eccentricity level even lower to minimize UMP and to reduce vibration and noise. In reality, both static and dynamic eccentricities tend to co-exist. An inherent level of static eccentricity exists even in newly manufactured machines due to manufacturing and assembly method, as has been reported by Dorrell et al. [54]. This causes a steady UMP in one direction. With usage, this may lead to bent rotor shaft, bearing wear and tear etc. This might result in some degree of dynamic eccentricity. Unless detected early, these effects may snowball into a stator to rotor hub causing a major breakdown of the machine [53].

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Fig. 7. Simulated normalized plots of the line current spectra of a 3-ph 3-hp 60-Hz induction motor with 38.46% static (top) and 20% dynamic eccentricity (bottom) with 2p = 4; R = 43. Slip = 0:029.

Fig. 8. Simulated normalized plots of the line current spectra of a 3-ph 3-hp 60-Hz induction motor with 38.46% static with Slip = 0:029 (top) and 40% dynamic eccentricity and Slip = 0:004 67 (bottom) with 2p = 4; R = 42. The other static eccentricity component is suppressed due to loading effects.

The presence of static and dynamic eccentricity can be detected using MCSA [1], [52]. The equation describing the frequency components of interest is (8) where in case of static eccentricity, and , in case of dynamic eccentricity ( is known as eccentricity order), is the fundamental supply frequency, is the number of rotor slots, is the slip, is the number of pole pairs, is any integer, and is the order of the stator time harmonics that are present in the power supply driving the motor ( , etc.). In case one of these harmonics is a multiple of three, it may not exist theoretically in the line current of a balanced three-phase machine. However, it has been shown by Nandi et al. [55] and Ferrah et al. [56] that only a particular combination of machine pole pairs and rotor slot number will give rise to significant only static or only dynamic eccentricity-related components. This relationship for a 3-ph integral slot 60 phase belt machine is given by (9) , and or or . where Equation (9) assumes only the fundamental eccentricity component in the permeance or inverse air-gap function [51], [56]. It , these components are very weak is to be noted that with and noticeable only under light load conditions. Simulated results with a four-pole, skewed, 43-rotor slot maare given in Fig. 7. chine, which conforms to (8) with Similar results with a four-pole, skewed, 42-rotor slot machine are given in Fig. 8. The effects of eccentricity on frequency components given by (7) seem to be much less pronounced for this machine. It has also been ascertained that machines generating principal slot harmonics (PSHs) will not give rise to these components with only static or only dynamic eccentricity. The pole pairs and rotor slot numbers for these machines (3 ph, integral slot, 60 phase belt) are related by (10) where

, and

or .

Fig. 9. Simulated, normalized line current spectra of 3-ph 3-hp 60-Hz skewed four-pole induction motors with different rotor slots and identical mixed eccentricity (SE = 38:46%; DE = 20%) machine around fundamental. From top to bottom R = 44; 43; 42. Slip = 0:029.

However, if both static and dynamic eccentricities exist together, low-frequency components near the fundamental [54], [57] given by (11) can also be detected for all machines (Fig. 9). These low-frequency components also give rise to high-frequency components as described by (8). However, these components are strong only for machines (Fig. 10) whose pole pairs and rotor slot numand (10). For machines described bers are related by (9) , they are rather weak (Fig. 11). Equations (9) by (9) with and (10) can be proved following [58]–[60]. Modeling-based approaches to detect eccentricity-related components in line current have been described in [55] and [57]. The simulation results obtained through the models can be corroborated by permeance analysis and experimental results.


Fig. 10. Simulated, normalized high-frequency line current spectra of 3-ph 3-hp 60-Hz four-pole induction motors with different rotor slots and identical mixed eccentricity (SE = 38:46%; DE = 20%) machine. From top to bottom R = 44; 43. Slip = 0:029.

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Fig. 12. Experimental line current spectra with R = 44; p = 4; SE = 38:46%; DE = inherent; slip = 0:029. Top row: healthy, Bottom row: faulty.

Fig. 11. Simulated, normalized high-frequency line current spectra of 3-ph 3-hp 60-Hz four-pole induction motors with 42 rotor slots and mixed eccentricity (SE = 38:46%; DE = 20%) machine. Slip = 0:02.

Fig. 12 shows a machine with under healthy and with 38.46% static eccentricity and inherent dynamic eccentricity. This machine should show no increase in components given by (8) and (11) and it does not. However, with (Fig. 13) with 50% static eccentricity clearly shows a significant rise in the aforementioned components. Of interest are the changes of the components given by (11). Both sidebands change almost equally, unlike what was reported in [54]. The results in [54] are also different from Fig. 12 as far as the components given by [11] are concerned. However, when mixed eccentricity was introduced in a machine, related harmonics given by (8) and (11) show an appreciable increase under no-load as well as full-load condition. Figs. 14–16 show these results. Loading seems to suppress some of the components. This machine falls into the category described by (10). Vibration signals can also be monitored to detect eccentricity-related faults. The high-frequency vibration components for static or dynamic eccentricity are given by [52] using an and are difequation similar to (7) (only the values of ferent). In case of mixed eccentricity, the low-frequency stator vibration components are given by (12)

Fig. 13. Experimental line current spectra with R = 45; p = 4; SE = 50%; DE = inherent; slip = 0:0193. Top row: healthy, Bottom row: faulty.

Fig. 14. Experimental line current spectra around fundamental, with R = 28; p = 4; SE = 41:37%; DE = 20:69%; slip = 0:0028. Top: healthy, Bottom: faulty.

However, vibration transducers are delicate and expensive. They also have special installation requirements to avoid damage due to shock and vibration.

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experimental results for synchronous machines with dynamic eccentricity-related faults. DC motor eccentricity has also been reported by Haji and Toliyat [64]. Eccentricity fault signatures can be extracted from torque data [43] using TSDM in conjunction with TSCFE-SS. The inductances of an eccentric induction machine [65] are computed by combining the WFA and magnetic equivalent circuit (MEC). Effects of load unbalance, angular (horizontal) misalignment, and radial (vertical) misalignment on the current sideband of a small induction machine were studied in [66]. III. AI-BASED MACHINE CONDITION MONITORING AND FAULT DIAGNOSIS Experimental line current spectra around PSH, with R = 28; p = 4; SE = 41:37%; DE = 20:69%; slip = 0:0028. Top: healthy, Bottom:

Fig. 15. faulty.

Fig. 16. Experimental line current spectra with R = 28; p = 4; SE = 41:37%; DE = 20:69%; slip = 0:022. Top: healthy, Bottom: faulty.

Fig. 17.

ANN-based fault diagnosis.

Time-stepping finite-element methods have been employed recently to compare simulated results with experimentally obtained static eccentricity components in line currents [53]. It should be noted that eccentricity should be modeled using the modified winding function approach (MWFA) [55], [57]. Using the winding function approach (WFA) gives unequal mutual inductances, leading to incorrect results [61]. Other approaches, such as monitoring the stator voltage and current Park’s vector (Cardoso and Saraiva [62]) to detect eccentricity in an induction motor, can also be found in the literature. Toliyat and Al-Nauim [63] have provided simulation and

Until recently, the prevalent fault detection technique has been MCSA. References [67] and [68] provide a good review of MCSA-based techniques. Apart from fast Fourier transform (FFT)-based techniques applied to line current; broken bar faults, stator faults, and dynamic eccentricity faults can also be detected using higher order spectrum (HOS); in particular, bispectrum and trispectrum, from a single sensor measurement of the radial EM vibration [69]. Both line- and inverter-driven machines have been tested. Artificial neural networks (ANNs), fuzzy, or neuro-fuzzy systems are now used extensively for speed, torque estimation, and solid-state drive control of both dc and ac machines. They are particularly suited for ac machines’ applications where the relationships between motor current and speed are nonlinear. These AI techniques are now being extended as a decision making tool to MCSA results for condition monitoring and fault detection of machines [5], [34], [71]–[74]. A neural net-based fault diagnosis system utilizing the stator current spectra is described in Fig. 17. The preprocessor extracts the frequency components of the sampled current data. Using the rule-based frequency filters, these frequency components are classified into four categories with a decreasing level of importance. Based on these rules, a neural network, which has been trained for all possible operating conditions of the machine, is used to classify the incoming data. A spectral signature that falls outside the trained clusters is marked as a potential motor fault. In order to prevent false diagnosis, the postprocessor sends an alarm only when fault signatures are observed persistently. This function is performed by maintaining a time history of the motor being monitored. Such a scheme has been successfully implemented [5] to diagnose bearing and unbalanced rotor faults of induction motors. References [75] and [76] describe a neural-network-based fault prediction scheme that does not require any machine parameter or speed information. Speed is estimated from measured terminal voltage and current. Induction machines of different power ratings can be accommodated using minimal tuning of the neural network. Detection effectiveness of 93% or more is achieved. Similarly, fuzzy-logic-based systems have been used [73] to classify broken-bar-related faults by categorizing the two sideband components (6) around the fundamental of the induction motor line current by a set of nine rules. Denoting the sideand , which are the two inputs of the system, bands as and the number of broken bars as the output of the system,


an example of these rules is “ If is small and is large, equals one broken bar.” The fuzzy logic system considered is the Mamdani type. The fuzzy inference is obtained by using the fuzzy implication min–max methods and the centroid defuzzifiand are cation technique. The membership functions for small, medium, and large. Other examples of motor fault detection using neural networks and fuzzy-logic techniques can be found in [77]. IV. CONCLUSION A brief review of bearing, stator, rotor, and eccentricity-related faults and their diagnosis has been presented in this paper. It is clear from various literature that noninvasive MCSA is by far the most preferred technique to diagnose faults. However, theoretical analysis and modeling of machine faults are indeed necessary to distinguish the relevant frequency components from the others that may be present due to time harmonics, machine saturation, etc. Other techniques for fault detection based on axial flux-based measurements, vibration analysis, transient current, and voltage monitoring, etc. have also been discussed. A section on automated fault detection has also been included. REFERENCES [1] P. Vas, Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines. Oxford, U.K.: Clarendon, 1993. [2] G. B. Kliman and J. Stein, “Induction motor fault detection via passive current monitoring,” in Proc. Int. Conf. Electrical Machines, Cambridge, MA, Aug. 1990, pp. 13–17. [3] S. Chen and T. A. Lipo, “Bearing currents and shaft voltages of an induction motor under hard- and soft-switching inverter excitation,” IEEE Trans. Ind. Appl., vol. 34, no. 5, pp. 1042–1048, Sep./Oct. 1998. [4] R. R. Schoen, T. G. Habetler, F. Kamran, and R. G. Bartheld, “Motor bearing damage detection using stator current monitoring,” IEEE Trans. Ind. Appl., vol. 31, no. 6, pp. 1274–1279, Nov./Dec. 1995. [5] R. R. Schoen, B. K. Lin, T. G. Habetler, J. H. Schlag, and S. Farag, “An unsupervised on-line system for induction motor fault detection using stator current monitoring,” IEEE Trans. Ind. Appl., vol. 31, no. 6, pp. 1280–1286, Nov./Dec. 1995. [6] B. Yazici, G. B. Kliman, W. J. Premerlani, R. A. Koegl, G. B. Robinson, and A. Abdel-Malek, “An adaptive, on-line, statistical method for bearing fault detection using stator current,” in Proc. IEEE Industry Applications Soc. Annual Meeting Conf., New Orleans, LA, 1997, pp. 213–220. [7] J. R. Stack, T. G. Habetler, and R. G. Harley, “Effects of machine speed on the development and detection of rolling element bearing faults,” IEEE Power Electron. Lett., vol. 1, no. 1, pp. 19–21, Mar. 2003. [8] S. A. McInerny and Y. Dai, “Basic vibration signal processing for bearing fault detection,” IEEE Trans. Educ., vol. 46, no. 1, pp. 149–156, Feb. 2003. [9] G. B. Kliman, W. J. Premerlani, R. A. Koegl, and D. Hoeweler, “A new approach to on-line fault detection in ac motors,” in Proc. IEEE Industry Applications Soc. Annual Meeting Conf., San Diego, CA, 1996, pp. 687–693. [10] P. J. Tavner and J. Penman, Condition Monitoring of Electrical Machines. Letchworth, U.K.: Res. Studies Press, 1987. [11] G. Stone and J. Kapler, “Stator winding monitoring,” IEEE Ind. Appl. Mag., vol. 4, no. 5, pp. 15–20, Sep./Oct. 1998. [12] J. Penman, H. G. Sedding, B. A. Lloyd, and W. T. Fink, “Detection and location of interturn short circuits in the stator windings of operating motors,” IEEE Trans. Energy Convers., vol. 9, no. 4, pp. 652–658, Dec. 1994. [13] H. Henao, C. Demian, and G. A. Capolino, “A frequency-domain detection of stator winding faults in induction machines using an external flux sensor,” IEEE Trans. Ind. Appl., vol. 39, no. 5, pp. 1272–1279, Sep./Oct. 2003.

727

[14] H. A. Toliyat and T. A. Lipo, “Transient analysis of cage induction machines under stator, rotor bar and end ring faults,” IEEE Trans. Energy Convers., vol. 10, no. 2, pp. 241–247, Jun. 1995. [15] S. Williamson and P. Mirzoian, “Analysis of cage induction motor with stator winding faults,” IEEE Trans. Energy Convers., vol. 10, no. 2, pp. 241–247, Jun. 1995. [16] J. L. Kohler, J. Sottile, and F. C. Trutt, “Alternatives for assessing the electrical integrity of induction motors,” IEEE Trans. Ind. Appl., vol. 28, no. 5, pp. 1109–1117, Sep./Oct. 1992. [17] M. Arkan, D. K. Perovic, and P. Unsworth, “Online stator fault diagnosis in induction motors,” Proc. Inst. Elect. Eng., Elect. Power Appl., vol. 148, no. 6, pp. 537–547, Nov. 2001. [18] L. Collamati, F. Filippetti, G. Franceschini, S. Pirani, and C. Tassoni, “Induction motor stator fault on-line diagnosis based on labview environment,” in Proc. Mediterranean Electrotechnical Conf., vol. 1, 1996, pp. 495–498. [19] C. J. Diste and R. Schiferl, “Using temperature, voltage, and/or, speed measurements to improve trending of induction motor rms currents in process control and diagnostics,” in Proc. IEEE Industry Applications Soc. Annual Meeting Conf., vol. 1, 1994, pp. 312–318. [20] C. Xianrong, V. Cocquempot, and C. Christophe, “A model of asynchronous machines for stator fault detection and isolation,” IEEE Trans. Ind. Electron., vol. 50, no. 3, pp. 578–584, Jun. 2003. [21] F. Zidani, M. E. H. Benbouzid, D. Diallo, and M. S. Nait-Said, “Induction motor stator faults diagnosis by a current Concordia pattern-based fuzzy decision system,” IEEE Trans. Energy Convers., vol. 18, no. 4, pp. 469–475, Dec. 2003. [22] G. Joksimovic and J. Penman, “The detection of interturn short circuits in the stator windings of operating motors,” in Proc. Annu. Conf. IEEE Industrial Electronics Soc., Aug. 31–Sep. 4 1998, pp. 1974–1979. [23] A. Stavrou, H. Sedding, and J. Penman, “Current monitoring for detecting inter-turn short circuits in induction motors,” in Proc. Int. Conf. Electric Machines Drives, May 9–12, 1999, pp. 345–347. [24] F. Briz, M. W. Degner, A. Zamarron, and J. M. Guerrero, “Online stator winding fault diagnosis in inverter-fed AC machines using high-frequency signal injection,” IEEE Trans. Ind. Appl., vol. 39, no. 4, pp. 1109–1117, Jul./Aug. 2003. [25] D. Kostic-Perovic, M. Arkan, and P. Unsworth, “Induction motor fault detection by space vector angular fluctuation,” in Proc. IEEE Industry Applications Conf., vol. 1, Oct. 8–12, 2000, pp. 388–394. [26] S. M. A. Cruz and A. J. M. Cardoso, “Diagnosis of stator inter-turn short circuits in DTC induction motor drives,” in Proc. IEEE Industry Applications Soc. Annual Meeting Conf., Salt Lake City, UT, 2003, pp. 1332–1339. [27] S. M. A. Cruz, A. J. M. Cardoso, and H. A. Toliyat, “Diagnosis of stator, rotor, and airgap eccentricity faults in three-phase induction motors based on the multiple reference frames theory,” in Proc. 38th Industry Applications Annual Meeting Conf., Salt Lake City, UT, Oct. 2003, pp. 1340–1346. [28] S. Nandi and H. A. Toliyat, “Novel frequency domain based technique to detect incipient stator inter-turn faults in induction machines using stator induced voltage after switch-off,” IEEE Trans. Ind. Appl., vol. 38, no. 1, pp. 101–109, Jan./Feb. 2002. [29] , “Performance analysis of a three phase induction motor under abnormal operating conditions,” in Proc. IEEE SPDEMPED, Carry-leRouet, France, 1997, pp. 54–59. [30] M. E. H. Benbouzid, M. Vieira, and C. Theys, “Induction motors’ faults detection and localization using stator current advanced signal processing techniques,” IEEE Trans. Power Electron., vol. 14, no. 1, pp. 14–22, Jan. 1999. [31] A. H. Bonnett and G. C. Soukup, “Rotor failures in squirrel cage induction motors,” IEEE Trans. Ind. Appl., vol. IA-22, no. 6, pp. 1165–1173, Nov./Dec. 1986. [32] G. B. Kliman, R. A. Koegl, J. Stein, R. D. Endicott, and M. W. Madden, “Noninvasive detection of broken rotor bars in operating induction motors,” IEEE Trans. Energy Convers., vol. 3, no. 4, pp. 873–879, Dec. 1988. [33] W. T. Thomson and I. D. Stewart, “On-line current monitoring for fault diagnosis in inverter fed induction motors,” in Proc. Inst. Elect. Eng., 3rd Int. Conf. Power Electronics Drives, London, U.K., 1988, pp. 432–435. [34] F. Filippetti, G. Franceschini, C. Tassoni, and P. Vas, “AI techniques in induction machines diagnosis including the speed ripple effect,” in Proc. IEEE Industry Applications Soc. Annu. Meeting Conf., San Diego, CA, Oct. 6–10, 1996, pp. 655–662. [35] N. M. Elkasabgy, A. R. Eastham, and G. E. Dawson, “Detection of broken bars in the cage rotor on an induction machine,” IEEE Trans. Ind. Appl., vol. 22, no. 6, pp. 165–171, Jan./Feb. 1992.

728


[36] B. G. Gaydon, “An instrument to detect induction motor rotor circuit defects by speed fluctuation measurements,” in Proc. Electric Test Measuring Instrumentation—Testmex Conf. Papers, 1979, pp. 5–8. [37] J. Penman, M. N. Dey, A. J. Tait, and W. E. Bryan, “Condition monitoring of electrical drives,” Proc. Inst. Elect. Eng. B, vol. 133, no. 3, pp. 142–148, May 1986. [38] K. R. Cho, J. H. Lang, and S. D. Umans, “Detection of broken rotor bars in induction motors using state and parameter estimation,” IEEE Trans. Ind. Appl., vol. 28, no. 3, pp. 702–709, May/Jun. 1992. [39] F. Filippetti, G. Franceschini, C. Tassoni, and P. Vas, “Broken bar detection in induction machines: Comparison between current spectrum approach and parameter estimation approach,” in Proc. IEEE Industry Applications Soc. Annual Meeting Conf., vol. 1, Denver, CO, 1994, pp. 95–102. [40] M. Haji and H. A. Toliyat, “Pattern recognition—A technique for induction machines rotor broken bar detection,” IEEE Trans. Energy Convers., vol. 16, no. 4, pp. 312–317, Dec. 2001. [41] J. F. Bangura and N. A. O. Demerdash, “Comparison between characterization and diagnosis of broken bars/endring connectors and air-gap eccentricities of induction motors in ASD’s using a coupled finite element-state space method,” IEEE Trans. Energy Convers., vol. 15, no. 1, pp. 48–56, Mar. 2000. [42] J. F. Bangura, R. J. Povinelli, N. A. O. Demerdash, and R. H. Brown, “Diagnostics of eccentricities and bar/end-ring connector breakages in polyphase induction motors through a combination of time-series data mining and time-stepping coupled FE-state-space techniques,” IEEE Trans. Ind. Appl., vol. 39, no. 4, pp. 1005–1013, Jul./Aug. 2003. , “Diagnostics of bar and end-ring connector breakage faults in [43] polyphase induction motors through a novel dual track of time-series data mining and time-stepping coupled FE-state space modeling,” IEEE Trans. Energy Convers., vol. 17, no. 1, pp. 39–46, Mar. 2002. [44] A. M. Trzynadlowski and E. Ritchie, “Comparative investigation of diagnostic media for induction machine: A case of rotor cage faults,” IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 1092–1099, Oct. 2000. [45] I. Kerzenbaum and C. F. Landy, “The existence of large inter-bar currents in three phase squirrel cage motors with rotor-bar and/or end-ring faults,” IEEE Trans. Power App. Syst., vol. PAS-103, no. 7, pp. 1854–1862, Jul. 1984. [46] R. F. Walliser and C. F. Landy, “Determination of interbar current effects in the detection of broken rotor bars in squirrel cage induction motors,” IEEE Trans. Energy Convers., vol. 9, no. 1, pp. 152–158, Mar. 1994. , “The existence of large inter-bar currents in three phase squirrel [47] cage motors with rotor-bar and/or end-ring faults,” IEEE Trans. Energy Convers., vol. 9, no. 1, pp. 159–164, Mar. 1994. [48] G. H. Muller and C. F. Landy, “A novel method to detect broken rotor bars in squirrel cage induction motors when interbar currents are present,” IEEE Trans. Energy Convers., vol. 18, no. 1, pp. 71–79, Mar. 2003. [49] J. Milimonfared, H. M. Kelk, S. Nandi, A. Der Minassians, and H. A. Toliyat, “A novel approach for broken rotor bar detection in cage induction motors,” IEEE Trans. Ind. Appl., vol. 35, no. 5, pp. 1000–1006, Sep./Oct. 1999. [50] J. Penman and A. Stavrou, “Broken rotor bars: Their effect on the transient performance of induction machines,” in Proc. IEEE Electric Power Applications, vol. 143, Nov. 1996, pp. 449–457. [51] B. Heller and V. Hamata, Harmonic Field Effects in Induction Machine. New York: Elsevier, 1977. [52] J. R. Cameron, W. T. Thomson, and A. B. Dow, “Vibration and current monitoring for detecting airgap eccentricity in large induction motors,” Proc. Inst. Elect. Eng. B, vol. 133, no. 3, pp. 155–163, May 1986. [53] A. Barbour and W. T. Thomson, “Finite element study of rotor slot designs with respect to current monitoring for detecting static airgap eccentricity in squirrel-cage induction motor,” in Proc. IEEE Industry Applications Soc. Annual Meeting Conf., New Orleans, LA, Oct. 5–8, 1997, pp. 112–119. [54] D. G. Dorrell, W. T. Thomson, and S. Roach, “Analysis of airgap flux, current, vibration signals as a function of the combination of static and dynamic airgap eccentricity in 3-phase induction motors,” IEEE Trans. Ind. Appl., vol. 33, no. 1, pp. 24–34, Jan./Feb. 1997. [55] S. Nandi, S. Ahmed, and H. A. Toliyat, “Detection of rotor slot and other eccentricity related harmonics in a three phase induction motor with different rotor cages,” IEEE Trans. Energy Convers., vol. 16, no. 3, pp. 253–260, Sep. 2001.

[56] A. Ferrah, P. J. Hogben-Liang, K. J. Bradley, G. M. Asher, and M. S. Woolfson, “The effect of rotor design of sensorless speed estimation using rotor slot harmonics identified by adaptive digital filtering using the maximum likelihood approach,” in Proc. IEEE Industry Applications Soc. Annu. Meeting Conf., New Orleans, LA, Oct. 5–8, 1997, pp. 128–135. [57] S. Nandi, R. M. Bharadwaj, and H. A. Toliyat, “Performance analysis of a threephaseinductionmotorunderincipientmixedeccentricitycondition,” IEEE Trans. Energy Convers., vol. 17, no. 3, pp. 392–399, Sep. 2002. [58] P. L. Alger, The Nature of Induction Machines. New York: Gordon and Breach, 1965. [59] G. Kron, Equivalent Circuits of Electric Machinery. New York: Wiley, 1951. [60] M. M. Liwschitz, “Field harmonics in induction motors,” Trans. Amer. Inst. Elect. Eng., vol. 61, pp. 797–803, Nov. 1942. [61] H. A. Toliyat, M. S. Arefeen, and A. G. Parlos, “A method for dynamic simulation of air-gap eccentricity in induction machines,” IEEE Trans. Ind. Appl., vol. 32, no. 4, pp. 910–918, Jul./Aug. 1996. [62] A. J. M. Cardoso and E. S. Saraiva, “Computer-aided detection of airgap eccentricity in operating three-phase induction motors by Park’s vector approach,” IEEE Trans. Ind. Appl., vol. 29, no. 5, pp. 897–901, Sep./Oct. 1993. [63] H. A. Toliyat and N. A. Al-Nuaim, “A novel method for modeling dynamic air-gap eccentricity in synchronous machines based on modified winding function theory,” IEEE Trans. Energy Convers., vol. 13, no. 2, pp. 156–162, Jun. 1998. [64] M. Haji and H. A. Toliyat, “Rotor eccentricity fault detection of a DC motor,” in Proc. Industrial Electronics Society Conf., Denver, CO, Nov. 29–Dec. 2, 2001, pp. 591–596. [65] H. Meshgin-Kelk, J. Milimonfared, and H. A. Toliyat, “A comprehensive method for the calculation of inductance coefficients of cage induction machines,” IEEE Trans. Energy Convers., vol. 18, no. 2, pp. 187–193, Jun. 2003. [66] R. R. Obaid, T. G. Habetler, and D. J. Gritter, “A simplified technique for detecting mechanical faults using stator current in small induction motors,” in Proc. IEEE Industry Applications Annu. Meeting Conf., Rome, Italy, Oct. 8–12, 2000, pp. 479–483. [67] M. E. H. Benbouzid and E. Hachemi, “A review of induction motors signature analysis as a medium for faults detection,” IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 984–993, Oct. 2000. [68] M. E. H. Benbouzid and G. B. Kliman, “What stator current processingbased technique to use for induction motor rotor faults diagnosis?,” IEEE Trans. Energy Convers., vol. 18, no. 2, pp. 238–244, Jun. 2003. [69] N. Arthur and J. Penman, “Induction machine condition monitoring with higher order spectra,” IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 1031–1041, Oct. 2000. [70] D. J. T. Siyambalapitiya and P. G. McLaren, “Reliability improvement and economic benefits of on-line monitoring systems for large induction machines,” IEEE Trans. Ind. Appl., vol. 26, no. 6, pp. 1018–1025, Nov./Dec. 1990. [71] F. Filippetti, G. Franceschini, and C. Tassoni, “Neural networks aided on-line diagnostics of induction motor faults,” in Proc. IEEE Industry Applications Soc. Annual Meeting Conf., vol. 1, Toronto, ON, Canada, 1993, pp. 316–323. [72] J. Penman and C. M. Yin, “Feasibility of using unsupervised learning, artificial neural networks for the condition monitoring of electrical machines,” Proc. Inst. Elect. Eng., Elect. Power Appl., vol. 141, no. 6, pp. 317–322, Nov. 1994. [73] P. Vas, Artificial—Intelligence-Based Electrical Machines and Drives: Applications of Fuzzy, Neural, Fuzzy-Neural and Genetic Algorithm Based Techniques. New York: Oxford Univ. Press, 1999. [74] F. Filippetti, G. Franceschini, C. Tassoni, and P. Vas, “Recent developments of induction motor drvies fault diagnosis using AI techniques,” IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 994–1004, Oct. 2000. [75] K. Kim, A. G. Parlos, and R. M. Bharadwaj, “Sensorless fault diagnosis of induction motors,” IEEE Trans. Ind. Electron., vol. 50, no. 5, pp. 1038–1051, Oct. 2003. [76] K. Kim and A. G. Parlos, “Induction motor fault diagnosis based on neuropredictors and wavelet signal processing,” IEEE/ASME Trans. Mechatronics, vol. 7, no. 2, pp. 201–219, Jun. 2002. [77] M. Y. Chow, Methodologies of Using Neural Network and Fuzzy Logic Technologies for Motor Incipient Fault Detection, Singapore: World Scientific, 1997.


Subhasis Nandi (S’97–M’00) received the B.E. degree in electrical engineering from Jadavpur University, Calcutta, India, in 1985, the M.E. degree in electrical engineering from the Indian Institute of Science, Bangalore, in 1988, and the Ph.D. degree in electrical engineering from Texas A&M University, College Station, in 2000. Currently, he is an Assistant Professor with the Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada. He has recently received a Canadian Foundation for Innovation (CFI) and a matching British Columbia Knowledge Development Fund (BCKDF) grant for setting up an advanced drives’ lab at the University of Victoria. From 1988 to 1996, he was with TVS Electronics and the Central Power Research Institute, Bangalore, India, working in the areas of power electronics and drives. His main research interests are power electronics and drives and analysis and design of electrical machines, with special emphasis on fault diagnosis.

Hamid A. Toliyat (S’87–M’91–SM’96) received the B.S. degree in electrical engineering from Sharif University of Technology, Tehran, Iran, in 1982, the M.S. degree in electrical engineering from West Virginia University, Morgantown, in 1986, and the Ph.D. degree in electrical engineering from the University of Wisconsin, Madison, in 1991. He joined the faculty of Ferdowsi University of Mashhad, Mashhad, Iran, as an Assistant Professor of electrical engineering. In 1994, he joined the Department of Electrical Engineering, Texas A&M University (TAMU), College Station, where he is currently E. D. Brockett Professor of Electrical Engineering. His main research interests and experience include analysis and design of electrical machines, variable speed drives for traction and propulsion applications, fault diagnosis of electric machinery, and sensorless variable speed drives. He has supervised more than 35 graduate students, published over 250 technical papers, presented more than 35 invited lectures all over the world, and has ten issued and pending U.S. patents in these fields. He is the author of DSP-Based Electromechanical Motion Control, (CRC, 2003) and Co-Editor of the Handbook of Electric Motors: 2nd Ed. (Marcel Dekker, 2004). Dr. Toliyat received the prestigious Cyrill Veinott Award in Electromechanical Energy Conversion Award from the IEEE Power Engineering Society in 2004, TEES Fellow Award in 2004, Distinguished Teaching Award in 2003, E.D. Brockett Professorship Award in 2002, Eugene Webb Faculty Fellow Award in 2000, and TAMU Select Young Investigator Award in 1999 from TAMU. He has also received the Space Act Award from NASA in 1999, and the Schlumberger Foundation Technical Awards in 2001 and 2000. He is also Chairman of IEEE-IAS Electric Machines Committee, and is a member of Sigma Xi. He is a senior member of the Power Engineering, Industrial Applications, Industrial Electronics, Power Electronics Societies of the IEEE, and the recipient of the 1996 IEEE Power Engineering Society Prize Paper Award for his paper on the Analysis of Concentrated Winding Induction Machines for Adjustable Speed Drive Applications-Experimental Results. He is an Editor of IEEE TRANSACTIONS ON ENERGY CONVERSION, and was an associate editor of IEEE TRANSACTIONS ON POWER ELECTRONICS.

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Xiaodong Li (S’02) received the B.Eng. degree in electrical engineering from Shanghai JiaoTong University, Shanghai, China, in 1994, and is currently pursuing the Ma.Sc. degree at the University of Victoria, Victoria, BC, Canada. In 1994, he became an Electrical Engineer with Hongwan Diesel Power Co., Zhuhai, China, the local power company in South China, where he conducted power generation system maintenance.