CONDITION MONITORING AND FAULT DIAGNOSIS OF INDUCTION MOTOR USING MOTOR CURRENT SIGNATURE ANALYSIS

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CONDITION MONITORING AND FAULT

DIAGNOSIS OF INDUCTION MOTOR USING

MOTOR CURRENT SIGNATURE ANALYSIS

A THESIS

SUBMITTED

FOR THE AWARD OF DEGREE OF

DOCTOR OF PHILOSOPHY

BY

NEELAM MEHALA

(REGISTRATION NO. 2K07-NITK-PhD-1160-E)

ELECTRICAL ENGINEERING DEPARTMENT

NATIONAL INSTITUTE OF TECHNOLOGY

KURUKSHETRA, INDIA

October, 2010

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CONDITION MONITORING AND FAULT

DIAGNOSIS OF INDUCTION MOTOR USING

MOTOR CURRENT SIGNATURE ANALYSIS

A THESIS

SUBMITTED

FOR THE AWARD OF DEGREE OF

DOCTOR OF PHILOSOPHY

BY

NEELAM MEHALA

(REGISTRATION NO. 2K07-NITK-PhD-1160-E)

UNDER THE SUPERVISION OF

DR. RATNA DAHIYA

ELECTRICAL ENGINEERING DEPARTMENT

NATIONAL INSTITUTE OF TECHNOLOGY

KURUKSHETRA, INDIA

October, 2010

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DECLARATION

I certify that

a. The work contained in this thesis is my own and has been done by me under the guidance of my supervisor.

b. The work has not been submitted to any other institute for any degree or diploma. c. I have followed the guidelines provided by the institute in preparing the thesis.

d. Whenever I have used material (data, theoretical analysis, figures and text) from other sources, I have given due credits by citing in the text of the thesis with details in the references.

Date: Neelam Mehala

(2K07-NITK-Ph.D.-1160-E)

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Certificate

Certified that the thesis entitled, “CONDITION MONITORING AND FAULT DIAGNOSIS OF INDUCTION MOTOR USING MOTOR CURRENT SIGNATURE ANALYSIS”, submitted by Ms.NEELAM MEHALA is in fulfillment of the requirements

for the award of the degree of DOCTOR OF PHILOSOPHY from NATIONAL INSTITUTE OF TECHNOLOGY, KURUKSHETRA. The candidate has worked under

my supervision. The work presented in this thesis has not been submitted for the award of any other degree/diploma.

Date:

Dr. Ratna Dahiya

Department of Electrical Engineering National Institute of Technology Kurukshetra (Haryana)

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Acknowledgements

During my Ph.D. study at National Institute of Technology Kurukshetra, I have been fortunate to receive valuable suggestions, guidance and support from my mentors, colleagues, family and friends.

First of all, I would like to express my most sincere gratitude to my supervisor Dr. Ratna Dahiya. She has been a wise and trusted guide throughout the entire process. Her guidance helped me to solve engineering problems and improve my communication. If it has not been for her vision, encouragement, and her confidence in my ability, much of this work would not have been completed.

I express my sincere gratitude and indebtedness to Dr. K.S. Sandhu, Chairman, Department of Electrical Engineering, National Institute of Technology

Kurukshetra for his moral support and continuous encouragement.

I must thank to Sh. Satpal and Sh. Suresh Kumar, Sr. Instructors, YMCA University of Science and Technology, Faridabad who were always available and willing to help with laboratory experimental set up.

I would like to thank my husband Dr. Vikas Kumar for his moral support and continuous encouragement. Finally, I extend my sincere gratitude to all those people who helped me in all their capacity to complete this work.

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ABSTRACT

Condition monitoring of induction motor have been a challenging task for the engineers and researchers mainly in industries. There are many condition monitoring methods, including vibration monitoring, thermal monitoring, chemical monitoring, acoustic emission monitoring but all these monitoring methods require expensive sensors or specialized tools whereas current monitoring out of all does not require additional sensors. Current monitoring techniques are usually applied to detect the various types of induction motor faults such as rotor fault, short winding fault, air gap eccentricity fault, bearing fault, load fault etc. In current monitoring, no additional sensors are necessary. This is because the basic electrical quantities associated with electromechanical plants such as current and voltage are readily measured by tapping into the existing voltage and current transformers that are always installed as part of the protection system. As a result, current monitoring is non-intrusive and may even be implemented in the motor control center remotely from the motors being monitored. Motor current signature analysis (MCSA) and Park's vector approach fall under current monitoring. The MCSA uses the current spectrum of the machine for locating characteristic fault frequencies. When a fault is present, the frequency spectrum of the line current becomes different from healthy motor. Such fault modulates the air-gap and produces rotating frequency harmonics in the self and mutual inductances of the machine. It depends upon locating specific harmonic component in the line current.

Extensive literature survey has been done for understanding the various faults and signal processing techniques available. It was observed that fault frequencies occur in the motor current spectra are unique for different motor faults. These fault frequencies can be easily detected with help of Motor Current Signature analysis (MCSA). Therefore, MCSA based techniques are used in present work for detection of common faults of induction motors. In addition, Park's vector approach is also applied for fault detection of induction motor. The proposed methods in this research allows continuous real time tracking of various types of faults in induction motors operating under continuous stationary and non stationary conditions. These methods recognize the fault signatures produced in induction motor and estimate the severity of the faults under different load conditions. The effects of these faults on motor current spectra of an induction motor are investigated through experiments. In order

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accurately repeat the measurements of current signals. In the present research work, LabVIEW software is used to diagnose the faults of induction motor with direct online monitoring. The experiments were conducted in four phases.

The first phase experimentally describes the effect of rotor faults on stator current of motor. Three algorithms are proposed to track and detect the rotor faults in induction motors operating under different load conditions: Fast Fourier Transform algorithm (FFT), Short Time Fourier transform algorithm and Wavelet Transform based multiresolution analysis algorithm. FFT Method is easy to implement. However, this method does not show the time information. This is a serious drawback of FFT. More interesting signals contain numerous transitory characteristics such as drift, trends, and abrupt changes. These characteristics are often the most important part of the signal, and the Fourier analysis is not suitable for their detection. Therefore, other methods for signal analysis such as STFT, Wavelet transform are subsequently used to detect the rotor faults experimentally.

The second phase investigates short winding faults of induction motor. A short turn fault in induction motor can result in complete failure and shut down of the machine unless the fault is detected early, and evasive action is taken. In the research, this fault has been detected successfully using four types of algorithms: FFT, Gabor Transform, Wavelet Transform, Park's Vector Approach.

The air gap eccentricity faults are studied in third phase of the research. Same experimental set up is used for this purpose. Special methods were applied to implement static eccentricity and dynamic eccentricity in induction motor. Experimental results show that it is possible to detect the presence of air-gap eccentricity in operating three phase induction motor, by computer aided monitoring of stator current. Qualitative information about severity of fault can be obtained by using power spectrum.

The forth phase of research work investigates the application of advanced signal processing techniques for detection of mechanical faults such as bearing faults and gear box faults. It is experimentally demonstrated that faults in bearings may be detected by monitoring the voltage/current of the stator. This may offer an inexpensive alternative to vibration diagnostics that require sensors which are expensive. It is observed that the characteristic frequencies are not visible in the power spectrum for a smaller size outer race fault and inner race fault. As severity of fault increases, the characteristic frequencies become

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visible. Wigner-Ville Distribition (WD) is also implemented for detection of bearing faults. In addition, Park’s Vector approach is also applied for detecting the bearing faults. It is verified from experiments that the Park’s vector current spectrum of healthy motor is different from the current spectrum of the motor having faulty bearing. To detect the gear box fault, an experiment has also been conducted. The results obtained from this experiment show that any fault in either the pinion or the driven wheel generates a harmonic component in the motor current spectrum which can be detected in power spectrum of induction motor. The conclusions, contributions, and recommendations are summarized at the end.

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Table of Contents

Acknowledgements

Abstract

List of Tables

List of Figures

CHAPTER 1 Introduction

1.1 Overview………..1

1.2 Objectives of research work……….3

1.3 Orientation...…. 6

CHAPTER 2 Literature Review

2.1Introduction……… …10

2.2 Induction motors………10

2.3 Need for condition monitoring………...12

2.4 Existing condition monitoring techniques………...13

2.4.1 Thermal monitoring………..14

2.4.2 Torque monitoring………15

2.4.3 Noise monitoring……….…..15

2.4.4 Vibration monitoring……….15

2.4.5 Electrical monitoring……….17

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2.4.5.2 Wavelet analysis...…....29

2.4.5.3 Current park’s vector……….….…….30

2.5 Softwares used for fault diagnosis……….32

2.6 Important observations………..….32

2.7 Chapter summary...……….…...35

CHAPTER 3 Common IM’s Faults and their diagnostic techniques

3.1 Introduction……….36

3.2 Faults in induction motors………...37

3.3 Electrical faults………37

3.3.1 Rotor faults………..37

3.3.2 Short turn faults………..38

3.4 Mechanical faults……….40

3.4.1 Air gap eccentricity……….40

3.4.2 Bearing Faults……….41

3.4.3 Load Faults………..42

3.5 Signal processing techniques for fault detection of induction motor………..43

3.6 Fast Fourier Transform (FFT)……….43

3.7 Spectrum through Time-Frequency methods………..46

3.7.1 Short Time Fourier Transform (STFT)………..46

3.7.2 Gabor Transform (GT)………...47

3.7.3 Wigner-Ville Distribution (WVD)……….49

3.8 Wavelet Transform(WT) ……….………..50

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3.8.2 Discrete wavelet transform (DWT) for multiresolution analysis (MRA)...53

3.9 Park’s vector approach...…..55

3.10 Chapter summary………...………..56

CHAPTER 4 Experimental Study of Rotor Faults of Induction Motor

4.1 Introduction……….58

4.1.1 Broken rotor bar analysis………59

4.1.2 Experimental set up………62

4.2 Broken rotor bar fault diagnosis using FFT based power spectrum………65

4.2.1 System representation using LabVIEW programming….………..66

4.2.2 Data acquisition parameters………....67

4.2.3 Observations and discussion………...68

4.3 Broken rotor fault diagnosis using Short Time Fourier Transform……….77

4.3.1 System representation using LabVIEW programming………...77

4.3.2 Observations and discussion………..78

4.4 Broken rotor Fault diagnosis using Wavelet Transform……….80

4.4.1 System representation using LabVIEW programming………..80

4.4.2 Observations and discussion………..81

4.5 Study of unbalance rotor……….85

4.6 Chapter summary……….…...87

CHAPTER 5 Diagnosis of Stator Winding Fault in Induction motor

5.1 Introduction………..89

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5.3 Diagnosis of stator winding faults using FFT based power spectrum...………91

5.3.1. Data acquisition parameters and LabVIEW programming……….92

5.3.2. Observations and discussion………....93

5.4 Stator winding fault diagnosis using Gabor Transform……….99

5.4.1 Data acquisition parameters and LabVIEW programming………...99

5.4.2 Observations and discussion ……….………...101

5.5 Stator winding fault analysis using Wavelet Transform………....102

5.5.1 Data acquisition parameters and LabVIEW programming………..102

5.5.2 Observations and discussion……….106

5.6 Park's Vector approach for diagnosis of short winding fault ………....106

5.6.1 Data acquisition parameters and LabVIEW programming………..106

5.6.2 Observations and discussion……….109

5.7 Chapter summary...………....109

CHAPTER 6 Detection of Air Gap Eccentricity Fault in Induction Motor

6.1 Introduction………..111

6.2 Air gap eccentricity………..112

6.3 Air gap eccentricity analysis ………...114

6.4 Air gap eccentricity detection using FFT based power spectrum………115

6.4.1 System representation using LabVIEW programming….………...116

6.4.2 Results and discussion……….117

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CHAPTER 7 Experimental Study of Bearing and Gear Box Faults of

Induction Motor

7.1 Introduction………...128

7.2 Bearing fault analysis………....129

7.3 Bearing fault analysis using FFT based power spectrum…...131

7.3.1 Data acquisition parameters and LabVIEW programming ...133

7.3.2 Results and discussion ……….133

7.4 Bearing fault detection using Wigner-Ville Distribution………...144

7.4.1 Data acquisition parameters and LabVIEW programming………...144

7.4.2 Results and discussion….……….146

7.5 Bearing fault detection using Park’s vector approach………...146

7.5.1 Data acquisition parameters and LabVIEW programming………..146

7.5.2 Results and discussion………..149

7.6 Gear box fault analysis………...149

7.7 Gear fault detection using Fast Fourier Transform………150

7.7.1 Experimental set up ………..150

7.7.2 Results and discussion………..…153

7.8 Chapter summary…...………155

CHAPTER 8 Conclusions, Contributions, and Recommendations

8.1 Introduction………156

8.2 Summary and Conclusions ………...157

8.3 Contributions……….160

8.4 Scope for future work……….…...163

References ………164

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List of Tables

Table 2.1 Statistics on motor faults/failure modes………...12

Table 4.1: Expected fault frequencies at various load condition……….61

Table 4.2: Parameters of experimental induction motor………..62

Table 4.3: Specifications of data acquisition card (NI-PCI 6251)………...63

Table 4.4: Data acquisition parameters………67

Table 4.5: Power spectrum analysis of one broken bar at various loading conditions………69

Table 4.6: Power spectrum analysis of five broken bars at various loading conditions……..69

Table 4.7: Power spectrum analysis of twelve broken bars at various loading conditions….70 Table 4.8: Data acquisition parameters………...77

Table 4.9: Decomposition details………81

Table 5.1: Expected fault frequencies at various load conditions………...91

Table 5.2: Experimental conditions for short winding fault detection………93

Table 5.3: Power spectrum analysis for short circuited winding fault………95

Table 5.4: Data acquisition parameters……….101

Table 6.1: Expected fault frequencies at various load conditions……….115

Table 6.2: Power spectrum analysis for 25% static eccentricity………...118

Table 6.3: Power spectrum analysis for 50% air gap eccentricity………119

Table 6.4: Power spectrum analysis for mixed eccentricity………..119

Table 7.1: Expected fault frequencies for inner race fault at various load conditions..……131

Table 7.2: Expected fault frequencies for outer race fault at various load conditions...……131

Table 7.3: Experimental conditions for bearing fault detection………134

Table 7.4: Power spectrum analysis for inner race fault of motor with 2mm hole…………134

Table 7.5: Power spectrum analysis for induction motor with 4mm inner race fault………135

Table 7.6: Power spectrum analysis for induction motor with 2mm outer race fault………136

Table 7.7: Power spectrum analysis for induction motor with 4mm outer race fault………136

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List of Figures

Figure 1.1: Research plan………..5

Figure 2.1: The process for fault diagnosis………..13

Figure 3.1: Various types of short winding faults………....39

Figure 3.2: Power spectrum of a healthy motor………...45

Figure 3.3: STFT of healthy motor………..47

Figure 3.4: Gabor spectrogram of a healthy motor………..48

Figure 3.5: WVD representation of a faulty motor………..49

Figure 3.6: Two channel perfect reconstruct filter………..52

Figure 3.7: Discrete Wavelet Transform……….52

Figure 3.8: Frequency range cover for details and final approximation………..54

Figure 3.9: Current Park’s vector for ideal condition………..56

Figure 4.1: Idealized current spectrum………60

Figure 4.2: Experimental set up………...64

Figure 4.3: Data acquisition card (PCI-6251)………. 64

Figure 4.4: Data acquisition board (ELVIS)………65

Figure 4.5: Motor fault detection and diagnosis system………..66

Figure 4.6: Block diagram for obtaining power spectrum using LabVIEW programming….67 Figure 4.7: Power spectrum of healthy motor at no load……….71

Figure 4.8: Power spectrum of faulty motor with 1 broken bar under no load condition…...71

Figure 4.9: Power spectrum of faulty motor with 5 broken bars under no load condition…..72

Figure 4.10: Power spectrum of faulty motor with 12 broken bars under no load condition..72

Figure 4.11: Power spectrum of healthy motor under half load………..73

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Figure 4.13: Power spectrum of faulty motor with 5 broken bars under half load………...74

Figure 4.14: Power spectrum of faulty motor with 12 broken bars under half load………...74

Figure 4.15: Power spectrum of healthy motor under full load ……….75

Figure 4.16: Power spectrum of faulty motor with 1 broken bar under full load…………...75

Figure 4.17: Power spectrum of faulty motor with 5 broken bars under full load…………..76

Figure 4.18: Power spectrum of faulty motor with 12 broken bars under full load…………76

Figure4.19:Block diagram for obtaining STFT spectrogram using LabVIEW programming ………...………...78

Figure 4.20: STFT spectrogram for healthy motor………..79

Figure 4.21: STFT spectrogram for faulty induction motor with broken bars………79

Figure 4.22: Block diagram for Multiresolution analysis using LabVIEW programming…..82

Figure 4.23: Multiresolution analysis for healthy motor……….83

Figure 4.24: Multiresolution analysis for faulty motor with broken bars………84

Figure 4.25: Slotted disc used in experiment………...85

Figure 4.26: Experimental set up……….86

Figure 4.27: Power spectrum of motor (Bolts placed on inner position of slotted disc)……….86

Figure 4.28: Power spectrum of motor (Bolts placed in outer position of slotted disc)……….87

Figure 5.1: Experimental set up………...92

Figure 5.2: Power spectrum of healthy motor under no load condition………..95

Figure 5.3: Power spectrum of faulty motor with 5% shortened under no load condition…..96

Figure 5.4: Power spectrum of faulty motor with 15% shortened under no load condition....96

Figure 5.5: Power spectrum of faulty motor with 30% shortened under no load condition....97

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Figure 5.7: Power spectrum of faulty motor (5% shortened) under full load………..98

Figure 5.8: Power spectrum of faulty motor (15% shortened) under full load………98

Figure 5.9: Power spectrum of faulty motor (30% shortened) under full load………99

Figure5.10:Block diagram for obtaining Gabor spectrogram using LabVIEW programming………...……….100

Figure 5.11: Gabor spectrogram for healthy induction motor………...100

Figure 5.12: Gabor spectrogram for short circuited induction motor………101

Figure 5.13: Block diagram for Multiresolution analysis using LabVIEW programming…103 Figure 5.14: Multiresolution analysis for healthy motor………...104

Figure 5.15: Multi resolution analysis for 30%short circuited induction motor………105

Figure 5.16: Block diagram for experimental detection system………....107

Figure 5.17: Block diagram for obtaining Current Park's vector pattern using LabVIEW programming…...107

Figure 5.18: Current Park’s vector pattern for healthy motor………...108

Figure 5.19: Current Park’s vector pattern for short circuited motor………....108

Figure 6.1: Healthy electric motor……….112

Figure 6.2: Difference between static and dynamic eccentricity………...113

Figure 6.3: Implementation of static eccentricity in induction motor………..115

Figure 6.4: Parts of motor machined for implementing air gap eccentricity……….116

Figure6.5:Block diagram for obtaining power spectrum using LabVIEW programming………117

Figure 6.6: Power spectrum of healthy motor under no load condition………120

Figure 6.7: Power spectrum of faulty motor with 25% static eccentricity under no load condition………...120

Figure 6.8: Power spectrum of faulty motor with 50% static eccentricity under no Load condition………..121

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Figure 6.9: Power spectrum of healthy motor under full load condition………...121

Figure 6.10: Power spectrum of faulty motor with 25% static eccentricity under full load..122

Figure 6.11: power spectrum of faulty motor with 50% eccentricity under full load……..122

Figure 6.12: Power spectrum of healthy motor under no load condition………..123

Figure 6.13: Power spectrum of faulty motor with mixed eccentricity under no load condition………...124

Figure 6.14: Power spectrum of healthy motor under full load………125

Figure 6.15: Power spectrum of healthy motor with mixed eccentricity under full Load….126 Figure 7.1: Ball bearing dimensions………..130

Figure 7.2: Inner race fault ………132

Figure 7.3: Outer race fault………....132

Figure 7.4: Power spectrum of healthy motor under no load condition………...137

Figure 7.5: Power spectrum of faulty motor with 2mm hole in inner race of bearing under no load condition (m=1)………137

Figure 7.6: Power spectrum of faulty motor with 2mm hole in inner race of bearing under no load condition (m=2)………....138

Figure 7.7: Power spectrum of faulty motor with 4mm hole in inner race of bearing under no load condition (m=1)………138

Figure 7.8: Power spectrum of faulty motor with 4mm hole in inner race of bearing under no load condition (m=2)………139

Figure 7.9: Power spectrum of healthy motor under full load condition……….139

Figure 7.10: Power spectrum of faulty motor with 2mm hole in inner race of bearing under full load condition……….140

Figure 7.11: Power spectrum of faulty motor with 4mm hole in inner race of bearing under full load condition……….140

Figure 7.12: Power spectrum of healthy motor under no load condition……….141 Figure 7.13: Power spectrum of faulty motor with 2mm hole in outer race of bearing under

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Figure 7.14: Power spectrum of faulty motor with 4mm hole in outer race of bearing under

no load condition………..142

Figure 7.15: Power spectrum of healthy motor under full load condition………142

Figure 7.16: Power spectrum of faulty motor with 2mm hole in outer race of bearing under full load condition……….143

Figure 7.17: Power spectrum of faulty motor with 4mm hole in outer race of bearing under full load condition……….143

Figure 7.18: Block diagram for obtaining Wigner-Ville Distribution (WVD) representation using LabVIEW programming……...144

Figure 7.19: Wigner-Ville Distribution (WVD) representation for motor with healthy bearing………..145

Figure 7.20: Wigner-Ville Distribution (WVD) representation for motor with faulty bearing (4mm hole in outer race)………..145

Figure 7.21: Block diagram for obtaining Current Park's vector pattern using LabVIEW programming………147

Figure 7.22: Current Park’s Vector pattern for healthy motor………...147

Figure 7.23: Current Park’s vector pattern for faulty bearing with 4 mm diameter hole in inner race………..148

Figure 7.24: Current Park vector’s pattern for faulty bearing with 4 mm diameter hole in outer race………...148

Figure 7.25: Worm and worm gear………151

Figure 7.26: Parts of gear box………151

Figure 7.27: Worm wheel with damage tooth………...152

Figure 7.28: Experimental set up………...152

Figure 7.29: Motor coupled with load………...153

Figure 7.30: Power spectrum for healthy gear box………....154

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CHAPTER 1

Introduction

1.1 Overview

The studies of induction motor behavior during abnormal conditions due to presence of faults and the possibility to diagnose these abnormal conditions have been a challenging topic for many electrical machine researchers. There are many condition monitoring methods including vibration monitoring, thermal monitoring, chemical monitoring, acoustic emission monitoring but all these monitoring methods require expensive sensors or specialized tools where as current monitoring out of all does not require additional sensors. This is because the basic electrical quantities associated with electromechanical plants such as current and voltage are readily measured by tapping into the existing voltage and current transformers that are always installed as part of the protection system. As a result, current monitoring is non-intrusive and may even be implemented in the motor control center remotely from the motors being monitored. [1-2].

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It is observed that the technique called ‘Motor Current Signature Analysis’ (MCSA) is based on current monitoring of induction motor; therefore it is not very expensive. The MCSA uses the current spectrum of the machine for locating characteristic fault frequencies. When a fault is present, the frequency spectrum of the line current becomes different from healthy motor. Such a fault modulates the air-gap and produces rotating frequency harmonics in the self and mutual inductances of the machine. It depends upon locating specific harmonic component in the line current [3-4]. Therefore, it offers significant implementation and economic benefits. In the research work, Motor Current Signature Analysis (MCSA) based methods are used to diagnose the common faults of induction motor such as broken bar fault, short winding fault, bearing fault, air gap eccentricity fault, and load faults. The proposed methods in the research allows continuous real time tracking of various types of faults in induction motors operating under continuous and variable loaded conditions. The effects of various faults on current spectrum of an induction motor are investigated through experiments.

The various advanced signal processing techniques such as Fast Fourier Transform, Short Time Fourier Transform, Gabor Transform, and Wavelet Transform are used to diagnose the faults of induction motor. A suitability of the signal for different type of faults is also discussed in detail. FFT is easy to implement but the drawback of this technique is that it is not suitable for analyzing transient signals. Although Short-Time Fourier Transform (STFT) can be used for analyzing transient signals using a time-frequency representation, but it can only analyze the signal with a fixed sized window for all frequencies, which leads to poor frequency resolution [5-6]. However, Wavelet Transform can overcome this problem by using a variable sized window.

In order to perform accurate and reliable analysis on induction motors, the installation of the motors and measurement of signal need to be accurate. Therefore, an experimental procedure and an experimental set up has been designed that can accurately repeat the measurements of signals and can introduce a particular fault to the motor in isolation of other faults. Stator current contains unique fault frequency components that can be used for detection of various faults of motor. Therefore, this research work investigates how the presence of common faults, such as rotor bar fault, short winding fault, air gap eccentricity, bearing fault, load fault, affects on different fault frequencies under different load conditions.

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In the research work, signal processing techniques are used for condition monitoring and fault detection of induction motors. The signal processing techniques have advantages that these are not computationally expensive, and these are simple to implement. Therefore, fault detection based on the signal processing techniques is suitable for an automated on-line condition monitoring system [7]. Signal processing techniques usually analyze and compare the magnitude of the fault frequency components, where the magnitude tends to increase as the severity of the fault increase. Therefore, the various signal processing techniques are used in present work for detection of common faults of induction motor. Signal processing techniques have their limitations. For example, the reliability of detecting the rotor fault using Fast Fourier Transform (FFT) depends on loading conditions and severity of fault. If the loading condition is too low or the fault is not too severe, Fast Fourier Transform may fail to identify the fault. Therefore, different techniques such as Wavelet Transform (WT) are investigated in the research work to find better features for detecting common faults under different loading conditions.

In present research work, twelve experiments are performed to diagnose the common faults of induction motors using six different currents monitoring techniques. The results and observations obtained are discussed and then final conclusions are made.

1.2 Objectives of research work

Literature review of condition monitoring and fault diagnosis of induction motor yields some important observations. It is observed that the faults can be diagnosed using any one of the signal processing techniques. Each signal processing technique can not be used for any type of faults. There is a need to compare the various signal processing techniques for a particular fault so that best suitable technique may be used to diagnose that particular fault.

The main aim of the research work is to diagnose the common electrical and mechanical faults experimentally with suitable signal processing techniques. It is observed that most of the work available in literature is based on MATLab programming which may be difficult at online monitoring. In the present research work, LabVIEW environment is used to diagnose the faults with direct online monitoring. LabVIEW software may be better option for direct interfacing with the system. Although some research work have been done

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by using LabVIEW also, but they have not diagnosed all common types of faults of induction motor.

In order to perform accurate and reliable analysis on induction motors, the installation of the motors and measurement of their signal need to be reliable. Therefore, the first aim of this thesis is to design an experimental procedure and an experimental set up that can accurately repeat the measurements of signals and can introduce a particular fault to the motor in isolation of other faults.

Stator current contains unique fault frequency components that can be used for detection of various faults of motor. The methods proposed in this research work allow continuous real time tracking of faults in induction motors operating under continuous stationary and non stationary conditions. Therefore, second aim of this research work is to investigate how the presence of common faults, such as rotor bar fault, short winding fault, air gap eccentricity, bearing fault, load fault, affect on different fault frequencies under different load conditions .

In this research work, condition monitoring and fault detection of induction motors is based on the signal processing techniques. The signal processing techniques have advantages that these are not computationally expensive and these are simple to implement. Therefore, fault detection based on the signal processing techniques is suitable for an automated on-line condition monitoring system. Signal processing techniques usually analyze and compare the magnitude of the fault frequency components, where the magnitude tends to increase as the severity of the fault increase. Therefore, the third aim of this thesis is to utilize the various signal processing techniques for detection of common faults of induction motor.

Signal processing techniques have their limitations. For example, some faults could be not diagnosed using Fast Fourier Transform, if the loading condition is too low or the fault is not too severe. Therefore, the final aim of this thesis is to investigate new features using different techniques such as Wavelet Transform (WT), to find better features for detecting common faults under different loading conditions.

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Figure 1.1: Research Plan

CONDITION MONITORING AND FAULT DIAGNOSIS OF

INDUCTION MOTOR

Literature Review

Common faults of induction motor MCSA based current monitoring techniques

Broken rotor bar fault Short winding fault Air gap Ecce. fault Bear ing failure Gear box fault FFT Wavelet transform STFT Park’s Vector Gabor transform Exp 1 Wigner distribution

Exp 2 Exp 3 Exp 4 Exp 5 Exp 6 Exp 7 Exp 8 Exp 9 Exp 10 Exp 11 Exp 12

Analysis and comparison of results obtained from experiments

Conclusions Exp=Experiment

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These objectives are addressed in four phases of research work:

The first phase experimentally describes the effects of rotor faults in the stator current of induction motor operating at different load conditions. To achieve this, the two types of rotor faults i.e. broken rotor bar fault and unbalance rotor fault are replicated in a laboratory and their effects on the spectrum of the motor current studied. This helps in better understanding the behavior of rotor faults in induction motors.

The second phase investigates short winding faults in stator winding of induction motor and their effects on the motor current spectrums. Based on this investigation, various signal processing methods to detect short winding fault of motor by monitoring the motor stator current are proposed and discussed.

The third phase of research work is focused on air gap eccentricity faults. In practice, all three-phase induction motors contain inherent static and dynamic eccentricity. They exist simultaneously in practice and are referred to as mixed eccentricity. Air gap eccentricity causes a ripple torque, which further leads to speed pulsations, vibrations, acoustic noise, and even an abrasion between the stator and rotor. Therefore, it is critical to detect air gap eccentricity as early as possible. To replicate the eccentricity fault in laboratory, special methods were used. The effects of eccentricity faults under different load conditions are studied to get the fault signature information.

The forth phase experimentally investigates the mechanical faults such as bearing fault and gear box fault. Gear defects and bearing defects are replicated in the laboratory and their effects on the motor current spectrum are studied with help of advanced signal processing techniques. Figure 1.1 illustrates the research plan for present work.

1.3 Orientation

The research work is presented in eight chapters of this thesis. Chapter 1 presents overview on condition monitoring of induction motors and objectives of research work along with the organization of the thesis.

Chapter 2 deals with the detailed literature survey and review of previous work on induction motor condition monitoring. It also provides the motivation to work on ‘common faults of induction machine’ and their ‘diagnostics techniques’.

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In chapter 3, common faults of induction motor such as rotor fault, short winding fault, air gap eccentricity fault, load fault and bearing fault has been introduced. Various signal processing techniques such as Fast Fourier Transform, Short Time Frequency Transform, Gabor Transform, Wigner-Ville Distribution and Wavelet Transform along with mathematical equation is given.

Experimental investigation of the rotor faults of induction motors operating under different load conditions is considered in chapter 4. The fault algorithm monitors the amplitude of fault frequencies and tracks changes in their amplitudes over time. Experiments are performed with using current based fault detection techniques such as Fast Fourier Transform, Short Time Fourier Transform, and Discrete Wavelet Transform. To diagnose the fault with these techniques, a laboratory test bench was set up. It consists of a three-phase squirrel cage induction machine coupled with rope brake dynamometer. The rated data of the tested three-phase squirrel cage induction machine were: 0.5 hp, 415V, 1.05 A and 1380(FL) r/min. The speed of the motor was measured by digital tachometer. The Virtual Instrument (VIs) was built up with programming in LabVIEW 8.2. This VIs was used both for controlling the test measurements and data acquisition, and for data processing. The data acquisition card (PCI-6251) and acquisition board (ELVIS) were used to acquire the current samples from the motor under different load conditions. In order to test the system in practical cases, several measurements were made, where the stator current of a machine with known number of broken rotor bars was read. Current measurements were performed for a healthy rotor and also for the same type of motor having different number of broken rotor bars. Tests were carried out for different loads with the healthy motor, and with similar motors having broken rotor bars. The rotor faults were provoked interrupting the rotor bars by drilling into the rotor. The measured current signals were processed using the Fast Fourier Transformation (FFT). Another experiment is performed to diagnose the broken rotor bar fault using STFT. Multiresolution analysis has also been applied to diagnose the broken rotor bar fault under varying load conditions. In addition, the effect of unbalance rotor is also studied in the research work. To unbalance the rotor, a slotted disc with attached weights is mounted on the shaft of motor. Then power spectrum is obtained using Virtual Instrument (VIs). This power spectrum is compared with power spectrum of healthy motor to search out the characteristic frequencies for studying the effect of unbalance rotor.

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Chapter 5 presents the experimental work for diagnosis of stator winding faults in induction motors operating under different load conditions. To diagnose the short winding fault, MCSA based fault detection techniques such as FFT, Gabor Transform, Wavelet Transform (WT) and Park’s vector approach are implemented. Several experiments were performed on motor under no load condition and with load coupled to shaft of motor. Short winding fault was diagnosed with FFT for 5%, 15% and 30% short circuit of winding. The results were compared to make the conclusions. After this, Gabor Transform and Wavelet Transform was applied to diagnose the same fault with 30% short circuit of winding. The Park’s vector approach was also introduced for detecting the short winding faults. An undamaged machine shows a perfect circle in Park’s vector representation whereas an unbalance due to winding faults results in an elliptic representation of the Park’s vector. The results obtained from the experiments present a great degree of reliability, which enables these techniques to be used as monitoring tool for short circuit fault of motor.

The air-gap eccentricity fault in three phase induction motor is discussed in chapter 6. The rated data of the tested three-phase squirrel cage induction machine were: 0.5 hp, 415V, 1.05 A and 1380(FL) r/min. To detect the eccentricity fault, Fast Fourier Transform (FFT) is implemented. It was very difficult to create air gap eccentricity fault in motor because air gap was very smaller in amount. Therefore, the special methods were used to replicate the air gap eccentricity fault in laboratory. Experimental results demonstrate the effectiveness of the proposed technique for detecting presence of air gap eccentricity in operating three phase induction machine. Qualitative information about severity of air gap eccentricity fault can be easily obtained by using FFT.

Chapter 7 proposes the experiments to investigate the load and bearing faults of induction motor and their effect on the motor current spectrums. Gear defects and bearing defects are replicated in the laboratory. The bearings were made failed by drilling the hole in inner race and outer race of the bearing with help of Electric Discharge Machine (EDM). Defective rolling element bearings generate eccentricity in the air gap with mechanical vibrations. The air gap eccentricities cause vibrations in the air gap flux density that produces visible changes in the stator current spectrum. The techniques such as FFT, Wigner-Ville Distribution, Park’s vector approach are applied to detect the bearing faults of motor. In the research work, an experiment has also been conducted to defect the load fault. The load fault

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is created by deforming gear’s tooth of gear box. The defective gear box (worm and worm gear) is coupled to motor with help of coupling and experiment was conducted. Whenever deformed tooth reaches the worm, the motor experience a ‘Bump’ in its load which gives rise to two frequency components symmetrically around the main frequency. This experiment verifies the fault in gear box coupled to motor by monitoring the current in induction motor. Chapter 8 presents the conclusion, contribution and scope for future work. The research investigates the applications of advanced signal processing techniques to detect various types of faults of motor such as rotor bar fault, stator winding fault, air gap eccentricity fault, bearing failure, and load fault. The research work helps in understanding the applications and limitations of fault detecting techniques. It is observed that LabVIEW is user friendly software and may be helpful in detecting the faults on and off line. It also helps in saving computational time of diagnosis. The new detecting methods proposed in this work are able to diagnose motor’s faults more sensitively and more reliably.

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CHAPTER 2

Literature Review

2.1 Introduction

In this chapter, the literature on condition monitoring of electric machine is reviewed. This review covers some important topics such as condition monitoring, fault diagnosis, thermal monitoring, vibration monitoring, electric monitoring, noise monitoring, motor current signature analysis, Current park’s vector approach, Fast Fourier Transform, STFT, Wavelet transform, signal processing techniques, etc. In addition, this review also covers the major developments in this field from early research to most recent.

2.2 Induction motors

Electrical machines are extensively used and core of most engineering system. These machines have been used in all kinds of industries. An induction machine is defined as an asynchronous machine that comprises a magnetic circuit which interlinks with two electric

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circuits, rotating with respect to each other and in which power is transferred from one circuit to the other by electromagnetic induction. It is an electromechanical energy conversion device in which the energy converts from electric to mechanical form [8]. The energy conversion depends upon the existence in nature of phenomena interrelating magnetic and electric fields on the one hand, and mechanical force and motion on the other. The rotor winding in induction motors can be squirrel-cage type or wound-rotor type. Thus, the induction motors are classified into two groups [9]:

• Squirrel-cage and

• Wound-rotor induction motors.

The squirrel cage induction motor consist of conducting bars embedded in slots in the rotor iron and short circuited at each end by conducting end rings. The rotor bars are usually made of copper, aluminum, magnesium or alloy placed in slots. Standard squirrel cage rotors have no insulation since bars carry large currents at low voltages. Another type of rotor, called a form-wound rotor, carries a poly phase winding similar to three phase stator winding. The terminals of the rotor winding are connected to three insulated slip rings mounted on the rotor shaft. In a form-wound rotor, slip rings are connected to an external variable resistance which can limit starting current and associated rotor heating. During start-up, inserting external resistance in the wound-rotor circuit produces a higher starting torque with less starting current than squirrel-cage rotors [9]. This is desirable for motors which must be started often.

The squirrel-cage induction motor is simpler, more economical, and more rugged than the wound-rotor induction motor. A squirrel-cage induction motor is a constant speed motor when connected to a constant voltage and constant frequency power supply. If the load torque increases, the speed drops by a very small amount. It is therefore suitable for use in constant-speed drive systems [8,9]. On the other hand, many industrial applications require several speeds or a continuously adjustable range of speeds. DC motors are traditionally used in adjustable drive systems. However, since DC motors are expensive, and require frequent maintenance of commutators and brushes. Squirrel-cage induction motors are preferred because they are cheap, rugged, have no commutators, and are suitable for high-speed applications. In addition, the availability of solid state controllers has also made possible to use squirrel-cage induction motors in variable speed drive systems. The squirrel-cage

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induction motor is widely used in both low performance and high performance drive applications because of its roughness and versatility.

Electric machines are frequently exposed to non-ideal or even detrimental operating environments. These circumstances include overload, insufficient lubrication, frequent motor starts/stops, inadequate cooling, etc. Under these conditions, electric motors are subjected to undesirable stresses, which put the motors under risk of faults or failures [10]. There is need to improve the reliability of motors due to their significant positions in applications. According to IEEE Standard 493-1997 [11], the most common faults and their statistical occurrences are listed in Table 1. This table is based on a survey on various motors in industrial applications. According to the table, most faults happen to bearings and windings. A 1985 statistical study by the Electric Power Research Institute (EPRI) provides similar results, i.e., bearing (41%), stator (37%), rotor (10%) and other (12%) [12]. Several contributions deal with these faults.

Table 2.1 Statistics on motor faults/failure modes [11]

Number of faults/failures Types of faults Induction

motor Synchronous motor Wound rotor motors

DC Motors All motors

Bearing 152 2 10 2 166

Winding 75 16 6 -- 97

Rotors 8 1 4 - 13

Shaft 19 - -- - 19

Brushes or slip rings -- 6 8 2 16

External device 40 7 1 - 18

Others 10 9 -- 2 51

2.3 Need for condition monitoring

Condition monitoring is defined as the continuous evaluation of the health of the plant and equipment throughout its service life. It is important to be able to detect faults while they are still developing. This is called incipient failure detection [1]. The incipient detection of motor failures also provides a safe operating environment. It is becoming increasingly important to use comprehensive condition monitoring schemes for continuous assessment of the electrical condition of electrical machines. By using the condition monitoring, it is possible to provide adequate warning of imminent failure. In addition, it is

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also possible to schedule future preventive maintenance and repair work. This can result in minimum down time and optimum maintenance schedules [2]. Condition monitoring and fault diagnosis scheme allows the machine operator to have the necessary spare parts before the machine is stripped down, thereby reducing outage times. Therefore, effective condition monitoring of electric machines is critical in improving the reliability, safety, and productivity.

2.4 Existing condition monitoring techniques

This research is focused on the condition monitoring and fault diagnosis of electric machines. Fault diagnosis is a determination of a specific fault that has occurred in system. A typical condition monitoring and fault diagnosis process usually consists of four phases as shown in Figure 2.1. Condition monitoring has great significance in the business environment due to following reasons [1,2]

• To reduce the cost of maintenance

• To predict the equipment failure

• To improve equipment and component reliability

• To optimize the equipment performance

• To improve the accuracy in failure prediction.

Figure 2.1: The process for fault diagnosis

Data acquisition

Feature extraction

Fault progression and trending analysis

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The condition monitoring of electrical and mechanical devices has been in practice for quite some time now. Several methods have evolved over time but the most prominent techniques are thermal monitoring, vibration monitoring, and electrical monitoring, noise monitoring, torque monitoring and flux monitoring.

2.4.1 Thermal monitoring

The thermal monitoring of electrical machines is accomplished either by measuring the local or bulk temperatures of the motor, or by parameter estimation. A stator current fault generates excessive heat in the shorted turns, and the heat promulgates the severity of the fault until it reaches a destructive stage. Therefore, some researcher developed thermal model of electric motors. Generally, thermal models of electric machines are classified into two categories [13]:

• Finite element Analysis based model

• Lumped parameter thermal models

FEA based models are more accurate, but highly computational intensive. A lumped parameter thermal model is equivalent to thermal network that is composed of thermal resistances, capacitances, and corresponding power losses. The accuracy of model is generally dependent on the number of thermally homogenous bodies used in model [13-14]. The parameters of lumped parameter model are usually determined in the two ways. The first is by using comprehensive knowledge of the motors, physical dimensions and construction materials. The second is to identify the parameters from extensive temperature measurement at different locations in the motor. Even though an electric machine is made of various materials that have different characteristics, the machine can be assumed to consist of several thermally homogenous lumped bodies. Based on these assumption, simplified model of an induction model and a PMSM consisting of two lumped thermal bodies are proposed in [15], and [16]. Likewise, Milanfar and Lang [17] developed a thermal model of electric machine. This thermal model is used to estimate the temperature of the motor and identify faults. Thermal monitoring can, in general, be used as an indirect method to detect some stator faults (turn-to-turn faults) and bearing faults. In a turn-to-turn fault, the temperature rises in the region of the fault, but this might be too slow to detect the incipient fault before it progresses into a more severe phase-to-phase or phase-to-neutral fault. In the case of

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detecting bearing faults, the increased bearing wear increases the friction and the temperature in that region of the machine. This increase in temperature of motor can be a detected by thermal monitoring.

2.4.2. Torque monitoring

All types of motor faults produce the sidebands at special frequencies in the air gap torque. However, it is not possible to measure the air gap torque directly. The difference between the estimated torques from the model gives anindication of the existence of broken bars. From the input terminals, the instantaneous power includes the charging and discharging energy in the windings. Therefore, the instantaneous power cannot represent the instantaneous torque. From the output terminals, the rotor, shaft, and mechanical load of a rotating machine constitute a torsional spring system that has its own natural frequency. The attenuations of the components of air gap torque transmitted through the torsional spring system are different for different harmonic orders of torque components [18].

2.4.3 Noise monitoring

Noise monitoring is done by measuring and analyzing the acoustic noise spectrum. Acoustic noise from air gap eccentricity in induction motors can be used for fault detection. However, the application of noise measurements in a plant is not practical because of the noisy background from other machines operating in the vicinity. This noise reduces the accuracy of fault detection using this method. Ellison and Yang [19] were detected the air gap eccentricity using this method. They verified from a test carried out in an anechoic chamber that slot harmonics in the acoustic noise spectra from a small power induction motor were functions of static eccentricity.

2.4.4 Vibration monitoring

All electric machines generate noise and vibration, and the analysis of the produced noise and vibration can be used to give information on the condition of the machine. Even very small amplitude of vibration of machine frame can produce high noise. Noise and vibration in electric machines are caused by forces which are of magnetic, mechanical and

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aerodynamic origin [20]. The largest sources of vibration and noise in electric machines are the radial forces due to the air gap field. Since the air gap flux density distribution is product of the resultant m.m.f. wave and total permeance wave. The resultant m.m.f. also contains the effect of possible rotor or stator asymmetries, and permeanance wave depends on the variation of the air gap as well , the resulting magnetic forces and vibrations are also depends on these asymmetries. Thus by analyzing the vibration signal of an electric machine, it is possible to detect various types of faults and asymmetries [22]. Bearing faults, rotor eccentricities, gear faults and unbalanced rotors are the best candidates for vibration based diagnostics. The vibration monitoring of electric machines is accomplished through the use of broad-band, narrow-band, or spectral (signature) analysis of the measured vibration energy of the machine. Vibration-based diagnostics is the best method for fault diagnosis, but needs expensive accelerometers and associated wiring. This limits its use in several applications, especially in small machines where cost plays a major factor in deciding the condition monitoring method.

Li et al. [23] carried out vibration monitoring for rolling bearing fault diagnoses. The final diagnoses are made with an artificial NN. The research was conducted with simulated vibration and real measurements. In both cases, the results indicate that a neural network can be an effective tool in the diagnosis of various motor bearing faults through the measurement and interpretation of bearing vibration signatures. In this study, the vibration features are obtained from the frequency domain using the FFT technique. Five vibration signatures are constructed. They are created from the power spectrum of the vibration signal and consist of the corresponding basic frequencies, with varying amplitudes based on the defect present. Time domain information, such as the maximum and mean value of the amplitude vibration waveform and the Kurtosis factor of the vibration waveform, are also considered. Thus, the complete neural network has six input measurements. Researchers showed how the neural network can be used effectively in the diagnosis of various motor bearing faults through appropriate measurement and interpretation of motor bearing vibration signals. In Jack & Nandi [24], there is an approach that brings better results. In this, the artificial neural network is helped by a genetic algorithm. In this study, statistical estimates of the vibration signal are considered as input features. The study examines the use of a genetic algorithm to select the most significant input features in the machine condition monitoring contexts. By doing this, a

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subset of six input features from a large set of possible features is selected, giving a very high classification accuracy of 99.8 %. Li et al. [23] and Jack & Nandi [24] are devoted to detecting mechanical faults; a similar approach could be extended to analyse the vibration pattern when an electrical machine is working with an electrical fault.

The major disadvantage of vibration monitoring is cost. For example, a regular vibration sensor costs several hundred dollars. A high product cost can be incurred just by employing the necessary vibration sensors for a large number of electric machines. Another disadvantage of vibration monitoring is that it requires access to the machine. For accurate measurements, sensors should be mounted tightly on the electric machines, and expertise is required in the mounting [25-27] In addition, sensors themselves may fail.

2.4.5 Electrical monitoring

Current Park’s vector, zero-sequence and negative-sequence current monitoring, and current signature analysis, all fall under the category of electrical monitoring. These methods are used stator current to detect various kind of machine and inverter faults. In most applications, the stator current of an induction motor is readily available since it is used to protect machines from destructive over-currents, ground current, etc. Therefore, current monitoring is a sensor-less detection method that can be implemented without any extra hardware [28].

2.4.5.1. Current signature Analysis

Numerous applications of using MCSA in equipment health monitoring have been published among the nuclear-generation, industrial, defense industries. In most applications, stator current is monitored for diagnosis of different faults of induction motor. Randy R. Schoen et. al. [29] addressed the application of motor current signature analysis for the detection of rolling-element bearing damage in induction machines. This study investigates the efficacy of current monitoring for bearing fault detection by correlating the relationship between vibration and current frequencies caused by incipient bearing failures. In this study, the bearing failure modes are reviewed and the characteristic bearing frequencies associated with the physical construction of the bearings are defined. The effects on the stator current spectrum are described and the related frequencies determined. Experimental results which

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are used to verify the relationship between the vibrational and current frequencies. The test results clearly illustrate that the stator current signature can be used to identify the presence of a bearing fault.

Randy R. Schoen [30] presented a method for on-line detection of incipient induction motor failures which requires no user interpretation of the motor current signature, even in the presence of unknown load and line conditions. A selective frequency filter learns the characteristic frequencies of the induction machine while operating under all normal load conditions. The generated frequency table is reduced to a manageable number through the use of a set of expert system rules based upon the known physical construction of the machine. This list of frequencies forms the neural network clustering algorithm inputs which are compared to the operational characteristics learned from the initial motor performance. This only requires that the machine be in “good” operating condition while training the system. Since a defect continues to degrade the current signature as it progresses over time, the system looks for those changes in the original learned spectra that are indicative of a fault condition and alarms when they have deviated by a sufficient amount. The combination of a rulebased (expert system) frequency filter and a neural network maximizes the system’s ability to detect the small spectral changes produced by incipient fault conditions. Compete failure detection algorithm was implemented and tested. An impending motor failure was simulated by introducing a rotating mechanical eccentricity to the test machine. After training the neural network, the system was able to readily detect the current spectral changes produced by the fault condition.

Schoen and Habetler [31-32] investigated the effects of a position-varying load torque on the detection of air gap eccentricity. The torque oscillations were found to cause the same harmonics as eccentricity. These harmonics are always much larger than eccentricity-related fault harmonics. Therefore, it was concluded that it is impossible to separate torque oscillations and eccentricity unless the angular position of the eccentricity fault with respect to the load torque characteristic is known.

Randy R. Schoen and Thomas G. Habetler [33] presented an analysis of the effects of position-varying loads on the current harmonic spectrum. The load torque-induced harmonics were shown to be coincidental with rotor fault-induced harmonics when the load varies synchronously with the rotor position. Furthermore, since the effect of the load and fault on a

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single stator current harmonic component is spatially dependent, the fault induced portion cannot be separated from the load portion. Therefore, any on-line detection scheme which measures the spectrum of a single phase of the stator current must rely on monitoring those spectral components which are not affected by the load torque oscillations.

John S. Hsu [18] suggested a method to monitor defects such as air gap eccentricity, cracked rotor bars and the shorted stator coils in induction motors. Air-gap torque can be calculated while the motor is running. No special down time for measurement is required. Data of the air-gap torque for a motor kept periodically for comparison purposes. Since more data than just a line current are taken, this method offers other potential possibilities that cannot be handled by examining only a Line current. Experiments conducted on a 5-hp motor showed the validity and potential of this approach.

Hamid A. Toliyat et. al. [34] developed a new induction machine model for studying static rotor eccentricity. It is based directly on the geometry of the induction machine and the physical layout of all windings. The model can simulate the performance of induction machines during transients as well as at steady state, including the effects of static rotor eccentricity. Since the dynamic model of the motor includes the mechanical equation, any arbitrary time function of load torque can be specified from which the resulting stator current is calculated. To illustrate the utility of this method, a conventional three phase induction motor with 50% rotor eccentricity was simulated. Digital computer simulations have been shown to yield satisfactory results which are in close agreement with experimental results of previous studies.

Stanislaw F. Legowski et. al. [35] has been demonstrated that the instantaneous electric power, proposed as a medium for signature analysis of induction motors, has definite advantages over the traditionally used current. The characteristic spectral component of the power appears directly at the frequency of disturbance, independently of the synchronous speed of the motor. This is important in automated diagnostic systems, in which the irrelevant frequency components, i.e. those at multiples of the supply frequency, are screened out.

Randy R. Schoen and Thomas G. Habetler [36] presented a method for removing the load torque effects from the current spectrum of an induction machine. They found that previously presented schemes for current-based condition monitoring ignore the load effect

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or assume that it is known. Therefore, a scheme for determining machine health in the presence of a varying load torque requires some method for separating the two effects. This is accomplished by comparing the actual stator current to a model reference value which includes the load effects. The difference between these two signals provides a filtered quantity, independent of variations of the load that allows continuous on-line condition monitoring conducted without concern for the load condition. Simulation results showed the effectiveness of this model reference estimation scheme at removing the load torque effects from the monitored spectra. Experimental results illustrated the feasibility of the proposed system. They demonstrated that the characteristic spectral components are present in the difference current and that the load effects can effectively be removed from the monitored spectrum to improve their detectability.

M.E.H. Benbouzid and H. Nejjari et. al. [37] stated that preventive maintenance of electric drive systems with induction motors involves monitoring of their operation for detection of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of the system. Intensive research effort has been for sometime focused on the motor current signature analysis. This technique utilizes the results of spectral analysis of the stator current. Reliable interpretation of the spectra is difficult, since distortions of the current waveform caused by the abnormalities in the drive system are usually minute. Their investigations show that the frequency signature of some asymmetrical motor faults can be well identified using the Fast Fourier Transform (FFT), leading to a better interpretation of the motor current spectra. Laboratory experiments indicate that the FFT based motor current signature analysis is a reliable tool for induction motor asymmetrical faults detection.

W. T. Thomson et. al. [38] presented an appraisal of on-line monitoring techniques to detect airgap eccentricity in three-phase induction motors. On-line current monitoring is proposed as the most applicable method in the industrial environment. The analyses of the current spectra for different motors are presented in the study. The results verify that the interpretation of the current spectrum proposed in this study was successful in diagnosing airgap eccentricity problems.

Birsen Yazıcı and Gerald B. Kliman [39] discussed an adaptive time–frequency method to detect broken bar and bearing defects. It was shown that the time–frequency spectrum reveals the properties relevant to fault detection better than the Fourier spectrum in

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References