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Non-invasive On-line Detection of Winding Faults in Induction Motors –A Review Hamidreza Behbahanifard 1* , Hamidreza Karshenas 1 , Alireza Sadoughi 1 1 Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran. *E-mail : [email protected] Abstract--This paper is a review and evaluation of different on-line methods for diagnosing winding fault in induction motors presented in literature. Many methods can be found in literature; in some references, frequency analysis of motor signals such as current , speed, instantaneous power , Park’s vector modulus and so on are introduced. Evaluation of negative sequence of current is also among the proposed methods. Supply voltage unbalance and some other phenomena may be confused with the winding fault. Therefore, the monitoring and fault detection of electrical machines have moved in recent years from traditional techniques to artificial intelligence based techniques. There are many other techniques that will be introduced and investigated in the detailed paper. A comparison of Different methods introduced in literature is the main objective of this paper. Index Terms-- Condition monitoring, Fault diagnosis, Induction motor, Stator fault, Turn fault. I. INTRODUCTION H u REE phase motors are critical components for electric tilities and process industries. These machines find a wide range of applications in most industries. Therefore, assessments of the running conditions and reliability of these motors is crucial to avoid unexpected failures. Several studies, conducted under the auspices of the IEEE and the Electric Power Research Institute (EPRI) have shown that stator winding fault is one of the major causes of motor failure [1]. About 35% of all reported induction motor failures fall into this category. Stator faults are usually related to an insulation failure. It is believed that these start as undetected turn-to-turn faults that finally grow and culminate in major ones [2]. Armature or stator insulation can fail due to several reasons. Among these are [3]: 1) Electrical discharges; 2) High temperatures in stator core or winding; 3) Slack core lamination, slot wedges, and joints, or loose bracing for end winding; 4) Short circuit or starting stresses; 5) Trouble or leakage in cooling systems; 6) Oil, moisture, and dirt contamination. II. FAULT DETECTION TECHNIQUES A. Magnetic Flux / Partial Discharge There are numerous methods to diagnose winding faults. For large machines, stator windings rated 4 kV and above, online partial-discharge tests give reliable results [4]. However, for low-voltage motors, stator fault detection procedures are yet to be standardized. Penman et al. [5] 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. There are frequency components in the axial flux component, which can be used for fault detection. The axial flux-based detection technique works well even in the presence of supply harmonics as in the case with VSI-driven induction motors [6]. B. Negative Sequence Components Imbalance in three phase systems maybe measured in terms of positive and negative sequence components in the supply voltage and motor current. Induction motor stator winding faults can be inferred from observation of negative sequence in motor phase currents. Toliyat and Lipo [7] have shown by experiments and modeling that winding 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 [8] and [9]. Several limitations to stator fault diagnosis by monitoring negative sequence current have to be considered carefully. Negative-sequence currents can also be caused by voltage unbalance, machine saturation, etc. Kliman et al. [2] modeled these unbalances which also include instrument asymmetries. It is reported that with these modifications, it is possible to detect a one turn fault. A similar technique has been used in [10] with a power decomposition technique 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 and the healthy conditions divided by the positive-sequence current under the healthy conditions is also reported to an effective diagnostic T 2008 International Conference on Condition Monitoring and Diagnosis, Beijing, China, April 21-24, 2008 978-1-4244-1622-6/08/$25.00 ©2007 IEEE

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Page 1: [IEEE 2008 International Conference on Condition Monitoring and Diagnosis - Beijing, China (2008.04.21-2008.04.24)] 2008 International Conference on Condition Monitoring and Diagnosis

Non-invasive On-line Detection of Winding Faults in Induction Motors –A Review

Hamidreza Behbahanifard1*, Hamidreza Karshenas1, Alireza Sadoughi1

1Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.

*E-mail : [email protected]

�Abstract--This paper is a review and evaluation of different

on-line methods for diagnosing winding fault in induction motors presented in literature. Many methods can be found in literature; in some references, frequency analysis of motor signals such as current , speed, instantaneous power , Park’s vector modulus and so on are introduced. Evaluation of negative sequence of current is also among the proposed methods. Supply voltage unbalance and some other phenomena may be confused with the winding fault. Therefore, the monitoring and fault detection of electrical machines have moved in recent years from traditional techniques to artificial intelligence based techniques. There are many other techniques that will be introduced and investigated in the detailed paper. A comparison of Different methods introduced in literature is the main objective of this paper.

Index Terms-- Condition monitoring, Fault diagnosis, Induction motor, Stator fault, Turn fault.

I. INTRODUCTION

Hu

REE phase motors are critical components for electric tilities and process industries. These machines find a

wide range of applications in most industries. Therefore, assessments of the running conditions and reliability of these motors is crucial to avoid unexpected failures. Several studies, conducted under the auspices of the IEEE and the Electric Power Research Institute (EPRI) have shown that stator winding fault is one of the major causes of motor failure [1]. About 35% of all reported induction motor failures fall into this category. Stator faults are usually related to an insulation failure. It is believed that these start as undetected turn-to-turn faults that finally grow and culminate in major ones [2]. Armature or stator insulation can fail due to several reasons. Among these are [3]: 1) Electrical discharges; 2) High temperatures in stator core or winding; 3) Slack core lamination, slot wedges, and joints, or loose

bracing for end winding; 4) Short circuit or starting stresses; 5) Trouble or leakage in cooling systems; 6) Oil, moisture, and dirt contamination.

II. FAULT DETECTION TECHNIQUES

A. Magnetic Flux / Partial Discharge

There are numerous methods to diagnose winding faults. For large machines, stator windings rated 4 kV and above, online partial-discharge tests give reliable results [4]. However, for low-voltage motors, stator fault detection procedures are yet to be standardized. Penman et al. [5] 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. There are frequency components in the axial flux component, which can be used for fault detection. The axial flux-based detection technique works well even in the presence of supply harmonics as in the case with VSI-driven induction motors [6].

B. Negative Sequence Components

Imbalance in three phase systems maybe measured in terms of positive and negative sequence components in the supply voltage and motor current. Induction motor stator winding faults can be inferred from observation of negative sequence in motor phase currents. Toliyat and Lipo [7] have shown by experiments and modeling that winding 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 [8] and [9]. Several limitations to stator fault diagnosis by monitoring negative sequence current have to be considered carefully. Negative-sequence currents can also be caused by voltage unbalance, machine saturation, etc. Kliman et al. [2] modeled these unbalances which also include instrument asymmetries. It is reported that with these modifications, it is possible to detect a one turn fault. A similar technique has been used in [10] with a power decomposition technique 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 and the healthy conditions divided by the positive-sequence current under the healthy conditions is also reported to an effective diagnostic

T

2008 International Conference on Condition Monitoring and Diagnosis, Beijing, China, April 21-24, 2008

978-1-4244-1622-6/08/$25.00 ©2007 IEEE

Page 2: [IEEE 2008 International Conference on Condition Monitoring and Diagnosis - Beijing, China (2008.04.21-2008.04.24)] 2008 International Conference on Condition Monitoring and Diagnosis

index [11]. Monitoring the change in positive-sequence current using the multiple reference frame theory was additionally suggested in [12] and [13] for detection. Stator negative sequence apparent impedance has been proposed as a fault feature with satisfactory sensitivity for the stator winding inter-turn short circuit fault (SWITSCF) in induction motors [14]. Meanwhile, the experiment results clearly indicate that for an actual motor operating on site, stator negative sequence apparent impedance is fluctuating with time to a certain extent, as would deteriorate the detection reliability and sensitivity. Therefore the low-pass-filtered stator negative sequence apparent impedance was chosen as the fault feature to reliably and sensitively detect SWITSCF in induction motors. Sang Bin Lee et al. [15] used a simple and robust sensor less technique for on-line stator winding turn fault detection based on monitoring an off-diagonal term of the sequence component impedance matrix.

C. Vibration

Various asymmetry faults in induction motors can be detected by monitoring the stator core vibration using instantaneous angular speed (IAS) techniques. In the case of stator winding fault or unbalanced supply, the vibration signal will contain a significant component with twice the supply frequency. For phase imbalance, the fault symptom is a significant increase in the 100-Hz component (if the supply frequency is 50 Hz) [16]. The stator frame vibration is a function of inter turn winding faults, single phasing, and supply-voltage unbalance [17]. The resonance between the exciting electromagnetic (EM) force and the stator is one of the main causes of noise production in electrical machines [17], [18], [19], [20]. In [21], a method has been described for detecting and diagnosing the type and magnitude of three induction machine fault conditions from the single sensor measurement of the radial electromagnetic machine vibration. The detection mechanism is based on the hypothesis that the induction machine can be considered as a simple system, and that the action of the fault conditions is to alter the output of the system in a characteristic and predictable fashion. Further, the change in output and fault condition can be correlated allowing explicit fault identification. Using this technique, there is no requirement for a priori data describing machine fault conditions, the method is equally applicable to both sinusoidal and inverter-fed induction machines and is generally invariant of both the induction machine load and speed.

D. Higher Order Statistics

Statistical process control methods have also been used to distinguish stator faults [22]. A model for evaluation and detection of stator turn–to-turn short-circuits faults in time domain has been reported in [23]. Motor Current Signature Analysis (MCSA) is the most widely used method for identifying faults in Induction Motors. MCSA focuses its efforts on the spectral analysis of stator

current. A few MCSA-based techniques for inter-turn stator fault detection have been reported [24]-[27]. 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 suchcomponents are not obviously introduced. Also, issues such as voltage unbalance, constructional imperfections that produce similar effects, are not addressed. Joksimovic and penman [28] developed a winding-function-base method for modeling poly phase cage induction motors with inter-turn short circuit in the machine stator winding. Analytical consideration which sheds light on some components of the stator current spectra of both healthy and faulty machines have developed.

E. Some Other Techniques

Stator fault detection using external signal injection is discussed in [29]. Measurement of the resulting high frequency negative sequence current (or alternatively of the negative sequence impedance) is used to detect turn faults at an incipient stage. Angular fluctuation of the stator current space vector [30] has been monitored in detecting stator inter-turn faults. It obtains data from stator current by exploring the position of the current space vector in the space vector plane. Variations of the phase angle of the vector are subjected to Fourier Transformation and analyzed in the frequency domain TheGoertzel algorithm is used for real-time implementation. A strong third-harmonic component can also be found in line current with stator inter-turn faults [12]. An online detection of induction motor stator winding fault is proposed in [31] using voltage mismatch detectors. The negative-sequence voltage mismatch detector appears to be slightly more sensitive to increasing deterioration severity; however, the absolute changes in the positive-sequence voltage mismatch detector are larger. Another online technique for monitoring the insulation condition of ac machine stator windings is proposed in [32]. The concept is to measure the differential leakage currents of each phase winding from the terminal box in a noninvasive manner to assess the insulation condition during motor operation. In [33] an approach is introduced, based on the spectral analysis of the current Park’s Vector modulus, for diagnosing stator winding faults in operating three phase synchronous and asynchronous motors. In this approach, the operating philosophy relies on the behavior of a spectral component at twice the fundamental supply frequency. The amplitude of this spectral component is directly related to the degree of asymmetry of the motor winding. Nandi and Toliyat [34] have observed that the shorted stator turns act as a search coil to pick up rotor magneto motive-force (mmf) harmonics in a squirrel cage machine. Although MCSA can detect these components, they may be confused with voltage unbalance in some machines. Fortunately, they can be detected at the terminal voltages of the machine just

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after switching it off. Detection of stator voltage unbalances and single phasing effects using signal processing techniques have been described in [35],[36].

F. AI-Based Techniques

AI techniques are now being extended as a decision making tool for condition monitoring and fault detection of machines [37]–[42]. The monitoring and fault detection of electrical machines have moved in recent years from traditional techniques to artificial intelligence (AI) techniques. Reduction of the human experts involvement in the diagnosis process has gradually taken place upon the recent developments in the modern artificial intelligence (AI) tools. Artificial neural networks (ANNs), fuzzy and adaptive-fuzzy systems, and expert systems are good candidates for the automation of the diagnostic procedures [43]. In [44], 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. In [45], an analytical redundancy method using neural network modeling of the induction motor in vibration spectra is proposed for machine fault detection and diagnosis. The short-time Fourier transform is used to process the quasi-steady vibration signals to continuous spectra for the neural network model training. The faults are detected from changes in the expectation of vibration spectra modeling error. Tallam et al. [46] presented an on line neural network based diagnostic scheme, for induction machine stator winding turn fault detection. The scheme consists of a feed-forward neural network combined with a Self-organizing Feature Map (SOFM) to visually display the operating condition of the machine on a two-dimensional grid. The operating point moves to a specific region on the map as a fault starts developing and can be used to alert the motor protection system to an incipient fault. In [47], three self-commissioning training algorithms are proposed for on-line training of a feed-forward NN to detect an induction machine stator winding turn-fault. The algorithms obviate the need for large data memory or computation requirements, which is a limitation of the conventionally used global training method. In [48] Two T-S fuzzy models are employed to detect turn fault, one is used to estimate the fault severity, the other is used to determine the exact number of fault turns. During fuzzy modeling, a fuzzy clustering algorithm based on similarity assessing is proposed to determine the optimal structure of the model and real coded genetic algorithm (GA) is adopted to online optimize model parameters. In [49], a model-based fault diagnosis system is developed for induction motors, using recurrent dynamic neural networks for transient response prediction and multi-resolution signal processing for no stationary signal feature extraction. In addition to nameplate information required for the initial setup, the proposed diagnosis system uses measured motor

terminal currents and voltages, and motor speed. The effectiveness of the diagnosis system is demonstrated through staged motor faults of electrical and mechanical origin.

III. CONCLUSION

A brief review and evaluation of different on-line methods for diagnosing stator winding fault in induction motors are presented and compared. Many methods can be found in literature; in some references, frequency analysis of motor signals such as current , speed, vibration, instantaneous power , Park’s vector modulus and so on are introduced. Evaluation of negative sequence of current is also among the proposed methods. Supply voltage unbalance and some other phenomena may be confused with the winding fault. Definitions and possible applications of the recently developed AI techniques are reported. There are many other techniques that is introduced and investigated in the detailed paper.

IV. REFERENCES

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