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

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

iii

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)

iv

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)

v

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.

vi

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

to perform the analysis on induction motors, an experimental set up is designed that can

vii

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

viii

visible. Wigner-Ville Distribition (WD) is also implemented for detection of bearing faults.

In addition, Parks Vector approach is also applied for detecting the bearing faults. It is

verified from experiments that the Parks 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.

ix

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 motors10

2.3 Need for condition monitoring...12

2.4 Existing condition monitoring techniques.....13

2.4.1 Thermal monitoring..14

2.4.2 Torque monitoring15

2.4.3 Noise monitoring...15

2.4.4 Vibration monitoring.15

2.4.5 Electrical monitoring.17

2.4.5.1. Current signature analysis..17

x

2.4.5.2 Wavelet analysis..............................................................................29

2.4.5.3 Current parks vector...30

2.5 Softwares used for fault diagnosis.32

2.6 Important observations...32

2.7 Chapter summary.......35

CHAPTER 3 Common IMs Faults and their diagnostic techniques

3.1 Introduction.36

3.2 Faults in induction motors...37

3.3 Electrical faults37

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

3.8.1 Discrete Wavelet Transform (DWT).... .50

xi

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

3.9 Parks 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 analysis59

4.1.2 Experimental set up62

4.2 Broken rotor bar fault diagnosis using FFT based power spectrum65

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

5.2 Stator winding faults90

xii

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 spectrum115

6.4.1 System representation using LabVIEW programming....116

6.4.2 Results and discussion.117

6.5 Chapter summary..127

xiii

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 Parks 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 Transform150

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 Introduction156

8.2 Summary and Conclusions ...157

8.3 Contributions.160

8.4 Scope for future work....163

References 164

List of publications from the research work..175

xiv

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 parameters67

Table 4.5: Power spectrum analysis of one broken bar at various loading conditions69

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 details81

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

Table 5.2: Experimental conditions for short winding fault detection93

Table 5.3: Power spectrum analysis for short circuited winding fault95

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 eccentricity119

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 detection134

Table 7.4: Power spectrum analysis for inner race fault of motor with 2mm hole134

Table 7.5: Power spectrum analysis for induction motor with 4mm inner race fault135

Table 7.6: Power spectrum analysis for induction motor with 2mm outer race fault136

Table 7.7: Power spectrum analysis for induction motor with 4mm outer race fault136

Table 8.1: Comparison of techniques applied for diagnosis of motor fault...162

xv

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 Parks vector for ideal condition..56

Figure 4.1: Idealized current spectrum60

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

Figure 4.12: Power spectrum of faulty motor with 1 broken bar under half load...73

xvi

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 load76

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 bars79

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 bars84

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

Figure 5.6: Power spectrum of healthy motor under full load.97

xvii

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 load98

Figure 5.9: Power spectrum of faulty motor (30% shortened) under full load99

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 motor101

Figure 5.13: Block diagram for Multiresolution analysis using LabVIEW programming103

Figure 5.14: Multiresolution analysis for healthy motor...104

Figure 5.15: Multi resolution analysis for 30%short circuited induction motor105

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 Parks vector pattern for healthy motor...108

Figure 5.19: Current Parks 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

programming117

Figure 6.6: Power spectrum of healthy motor under no load condition120

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

xviii

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 load125

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

no load condition..141

xix

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 condition142

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

programming147

Figure 7.22: Current Parks Vector pattern for healthy motor......147

Figure 7.23: Current Parks vector pattern for faulty bearing with 4 mm diameter hole in

inner race..148

Figure 7.24: Current Park vectors pattern for faulty bearing with 4 mm diameter hole in

outer race...148

Figure 7.25: Worm and worm gear151

Figure 7.26: Parts of gear box151

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

Figure 7.31: Power spectrum of motor with faulty gear box.154

1

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].

2

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.

3

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

4

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.

5

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 Parks Vector

Gabor transform

Exp 1

Wigner distribution

Exp 2 Exp 4 Exp 3 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

6

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.

7

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.

8

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 Parks 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

Parks vector approach was also introduced for detecting the short winding faults. An

undamaged machine shows a perfect circle in Parks vector representation whereas an

unbalance due to winding faults results in an elliptic representation of the Parks 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, Parks 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

9

is created by deforming gears 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 motors faults more sensitively and more reliably.

10

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 parks 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

11

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

12

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

13

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

Decision making

14

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

15

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 an indication 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

16

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

17

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 Parks 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

show the vibration and current spectra of an induction machine with different bearing faults

18

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 systems

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

19

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

20

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 Yazc and Gerald B. Kliman [39] discussed an adaptive timefrequency

method to detect broken bar and bearing defects. It was shown that the timefrequency

spectrum reveals the properties relevant to fault detection better than the Fourier spectrum in

21

the transform domain. The method is based on a training approach in which all the distinct

normal operating modes of the motor are learned before the actual testing starts. This study

suggests that segmenting the data into homogenous normal operating modes is necessary,

because different operating modes exhibit different statistical properties due to non stationary

nature of the motor current. Overlooking this fact will deteriorate the performance of the

detection. The result of this study showed that signals from faulty motors are several hundred

standard deviations away from the normal operating modes, which indicates the power of the

proposed statistical approach. Finally, it was suggested that the proposed method is a

mathematically general and powerful one which can be utilized to detect any fault that could

show up in the motor current.

Jafar Milimonfared et. al. [40] presented a new method for detecting broken-rotor-bar

faults by analyzing the stator-induced voltage after removing the mains. The method is

attractive because source non-idealities like unbalance time harmonics will not influence the

detection. Also it is clear from the nature of the test that it can be performed even with an

unloaded machine. Harmonic components predicted by theoretical analysis are clearly

matched by simulation results. However, due to inherent asymmetries of the machine, some

of these components may already exist, even in a healthy machine. It is also apparent from

the simulations and experiments that, although the number of broken bars does not have

much effect on the magnitude of the harmonic components, one can distinguish between a

faulty and a healthy machine. Interbar currents, dependence of the spectral amplitude on the

instance of disconnection, and short length of data also adversely affect on the detection

technique.

Benbouzid et. al. [7, 37] investigated the efficacy of current spectral analysis on

induction motor fault detection. The frequency signatures of some asymmetrical motor faults,

including air gap eccentricity, broken bars, shaft speed oscillation, rotor asymmetry, and

bearing failure, were identified. This work verified the feasibility of current spectral analysis.

Current spectral analysis was applied to other types of electrical machines too. For example,

Thomson [38,41] verified that the use of the current spectrum was successful in diagnosing

air gap eccentricity problems in large, high-voltage, three-phase induction motors. Le Roux

[42] monitored the current harmonic component at the rotating frequency (0.5 harmonic) to

detect the rotor faults of a permanent magnet synchronous machine.

22

Alberto Bellini et. al. [43] presented the impact of control on faulted induction

machine behavior. The diagnostic indexes usually used for open-loop operation are no longer

effective. Simulation and experimental results show that the spectrum of the field current

component in a field-oriented controlled machine has suitable features that can lead to an

effective diagnostic procedure. Specifically, in the case of stator and rotor faults, the

spectrum components at frequencies 2f and 2sf respectively, are quite independent of control

parameters and dependent on the fault extent.

Benbouzid [5] made a review of MCSA as a medium for fault detection. This study

introduces in a concise manner the motor signature analysis for the detection and localization

of abnormal electrical and mechanical conditions that indicate, or may lead to a failure of

induction motors. The MCSA utilizes the results of spectral analysis of the stator current for

the detection of airgap eccentricity, broken rotor bars and bearing damage. It is based on the

behavior of the current at the side band associated with the fault. For that, intimate

knowledge of the machine construction is required. It is explained that when the load torque

varies with rotor position, the current will contain spectral components, which coincide with

those caused by the fault condition. The torque oscillation results in stator current harmonics

that can obscure, and often overwhelm, those produced by the fault condition. Researcher

concluded that Fourier analysis is very useful for many applications where the signals are

stationary. However, it is not appropriate for analyzing a signal that has a transitory

characteristic such as drifts, abrupt changes and frequency trends. To overcome this problem,

Fourier analysis has been adapted to analyze small sections of the signal in time; this

technique is known as the short time fast Fourier transform (STFFT). STFT represents a sort

of compromise between time- and frequency-based views of a signal and provides

information about both.

Joksimovic & Penman [44] studied the interaction between faulty stator winding and

a healthy rotor cage. The faulty asymmetric stator winding may produce spatial harmonics

into the air-gap field. However, all these harmonics vary at a single frequency, i.e. the supply

frequency of the sinusoidal voltage source. The stator harmonics induce currents in the rotor

cage and reflect back from the rotor as new air-gap field harmonics. The air-gap harmonics

caused by the induced rotor currents vary at specific frequencies. The air-gap field harmonics

induce electromotive forces in the stator winding and generate harmonic stator currents at

23

these same frequencies. These are the same frequencies at which a healthy machine produces

harmonic stator currents. According to this analysis, a stator fault may generate only

harmonic stator currents, which vary at the fundamental and rotor-slot harmonic frequencies.

A fault in a stator winding may change the amplitudes of the stator-current harmonics, but it

will not produce any new frequencies in the stator-current spectrum. This significant result

implies that it may be difficult to detect a stator fault from a current spectrum using current

signature analysis.

Masoud Haji, and Hamid A. Toliyat [45] developed a pattern recognition technique

based on Bayes minimum error classifier to detect broken rotor bar faults in induction motors

at the steady state. The proposed algorithm uses only stator currents as input without the need

for any other variables. First rotor speed is estimated from the stator currents, then

appropriate features are extracted. Once normalized mean and variance plus mean and

covariance of each class are determined for an ac induction motor, the technique can be used

in online condition monitoring of the motor. Theoretical approach plus experimental results

from a 3 hp induction motor show the strength of the proposed method. Without loss of

generality, the algorithm can be revised to include other faults such as eccentricity and phase

unbalance.

Arkan et al. [46] presented a non-invasive online method for the detection of stator

winding faults in three-phase induction motors from the observation of the negative sequence

supply current. A power decomposition technique (PDT) was used to derive positive and

negative sequence components of measured voltages and currents. This study carried out

experimental studies, which showed that the negative sequence impedance could vary

between 10 % and 50 % during an inter-turn short circuit.

Tallam, Habetler, and Harley [47] monitored the negative-sequence voltage to detect

a turn-to-turn short circuit in a closed-loop drive-connected induction motor. A neural

network was used to learn and to estimate the negative-sequence voltage of a healthy motor,

which is used as the threshold. This helped to reduce the effects of machine non-ideality and

unbalanced supply voltage. According to [47], most of the turn-to-turn short circuit-related

fault signatures exist in the stator voltage because of the regulation of the drive controllers.

However, the influence of mechanical load was neglected. In practice, the distribution of

fault information between the stator voltage and current depends on drive controllers, as well

24

as mechanical load and operating conditions. Monitoring either stator current or voltage

alone cannot ensure an accurate prediction of motor conditions.

Miletic and Cettolo [48] acknowledged that Motor Current Signature Analysis

(MCSA) is one of the widely used diagnostic methods. This method is based on

measurement of sidebands in the stator current spectrum. These sidebands are usually located

close to the main supply frequency. Frequency converter causes supply frequency to slightly

vary in time and, as a result, some additional harmonics in the current spectrum are induced

and sidebands are reduced. These harmonics can be easily misinterpreted as the sidebands

caused by the rotor faults. In this study, the experimental results of fault diagnosis carried out

using standard supply and using frequency converter were compared and presented. All tests

were performed on 22 kW induction motor.

In current spectral analysis, the actual harmonics measured from a running machine

are always compared with known values (thresholds) obtained from a healthy motor. In

practical applications, the thresholds change with motor operating conditions. Therefore,

Obaid [49] proposed tracking the normal values of a healthy motor at different load

conditions. For each load condition, a corresponding threshold was determined and compared

with the on-line measurement to determine the motor condition. Besides the FFT technique

in spectral analysis, other techniques in advanced digital signal processing and pattern

recognition were applied to motor condition monitoring as well.

Mohamed El Hachemi Benbouzid, and Gerald B. Kliman [50] briefly presented

signal (mainly motor current) processing techniques for induction motor rotor fault detection

(mainly broken bars and bearing deterioration). The main advantages and drawbacks of the

presented techniques are also briefly discussed. In many cases, the conventional steady state

techniques may suffice. From the discussions, it appears that, for the most difficult cases,

time-frequency and time-scale transformations, such as wavelets, provide a more optimal

tool for the detection and the diagnosis of faulty induction motor rotors. On the one hand,

they remedy the main drawbacks of motor current signal processing techniques for fault

detection (i.e., nonstationarity). These techniques exhibit some interesting application

advantages, such as for coal crushers, where speed varies rapidly and for deteriorated

bearings where speed and signatures may vary in an unpredictable manner.

25

Szab Lornd et.al. [51] presented some results on detecting broken rotor bars in

induction motors. Five different motor conditions were studied (the healthy machine and

having up to 4 broken bars), each at 9 different loads. The results of this study show that if

there is any broken bar in the rotor it will directly affect the induced voltages in the stator

windings and the waveform of the stator currents. Therefore the spectrum analysis of the line

current (motor current signature analysis) is one of the best non-intrusive methods.

Szab Lornd et. al. [52] utilized the result of spectral analysis of stator current to

diagnose rotor faults. The diagnosis procedure was performed by using virtual

instrumentation (VIs). Several virtual instruments (VIs) were built up in Labview. These VIs

were used both for controlling the test measurements and data acquisition and for the data

processing. The tests were carried out for seven different loads with healthy motor, and with

similar motors having up to 5 broken rotor bars. The rotor bars were provoked interrupting

the rotor bars by drilling into the rotor. The measured current signals were processed using

the Fast Fourier Transformation (FFT). The power density of the measured phase current was

plotted. The results obtained for the healthy motor and those having rotor faults were

compared, especially looking for the sidebands components having the special frequencies.

The significance presence of some well defined sidebands frequencies in the harmonic

spectrum of the measured line current clearly indicated the rotor faults of the induction

machine.

Jason R. Stack et. al. [53] introduced the notion of categorizing bearing faults as

either single-point defects or generalized roughness. This is important because it divides

these faults according to the type of fault signatures they produce rather than the physical

location of the fault. The benefit of this categorization is twofold. First, it ensures that the

faults categorized as generalized roughness are not overlooked. The majority of bearing

condition monitoring schemes in the literature focus on detection of single-point defects.

While this is an important class of faults, a comprehensive and robust scheme must be able to

detect both generalized roughness and single-point defect bearing faults. Second, grouping

faults according to the type of fault signature they produce provides a clearer understanding

of how these faults should be detected. This provides improved insight into how bearing

condition monitoring schemes should be designed and applied. Experimental results obtained

26

from this research suggest generalized roughness faults produce unpredictable (and often

broadband) changes in the machine vibration and stator current.

Jason R. Stack et. al. [54] proposed a method for detecting developing bearing faults

via stator current. Current-based condition monitoring offers significant economic savings

and implementation advantages over vibration-based techniques. This method begins by

filtering the stator current to remove most of the significant frequency content unrelated to

bearing faults. Afterwards, the filtered stator current is used to train an autoregressive signal

model. This model is first trained while the bearings are healthy, and a baseline spectrum is

computed. As bearing health degrades, the modeled spectrum deviates from its baseline value;

the mean spectral deviation is then used as the fault index. This fault index is able to track

changes in machine vibration due to developing bearing faults. Due to the initial filtering

process, this method is robust to many influences including variations in supply voltage,

cyclical load torque variations, and other (nonbearing) fault sources. Experimental results

from ten different bearings are used to verify the proficiency of this method.

Srgio M. A. Cruz and A. J. Marques Cardoso [55] proposed two different methods

for the diagnosis of stator faults in DTC induction motor drives. Through a qualitative

analysis of the phenomena involving the behavior of this type of drive after the occurrence of

a stator fault in the motor, it was demonstrated that the flux and torque hysteresis controllers

tend to introduce a significant third harmonic component in the motor supply currents. The

presence of a strong third harmonic component in the motor supply currents is thus an

indicator about the presence of a stator fault. The results obtained with this diagnostic

technique demonstrated its effectiveness for the detection and quantification of the extension

of the fault in DTC induction motor drives.

Humberto Henao et. al. [56] presented the experimental and the analytical validation

of the equivalent internal circuit approach applied to the three-phase squirrel-cage induction

machine.