Electric machines : modeling, condition monitoring, and fault diagnosis

With countless electric motors being used in daily life, in everything from transportation and medical treatment to military operation and communication, unexpected failures can lead to the loss of valuable human life or a costly standstill in industry. To prevent this, it is important to precisely detect or continuously monitor the working condition of a motor. Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis reviews diagnosis technologies and provides an application guide for readers who want to research, develop, and implement a more effective fault diagnosis and condition monitoring scheme--thus improving safety and reliability in electric motor operation. It also supplies a solid foundation in the fundamentals of fault cause and effect.

Combines Theoretical Analysis and Practical Application

Written by experts in electrical engineering, the book approaches the fault diagnosis of electrical motors through the process of theoretical analysis and practical application. It begins by explaining how to analyze the fundamentals of machine failure using the winding functions method, the magnetic equivalent circuit method, and finite element analysis. It then examines how to implement fault diagnosis using techniques such as the motor current signature analysis (MCSA) method, frequency domain method, model-based techniques, and a pattern recognition scheme. Emphasizing the MCSA implementation method, the authors discuss robust signal processing techniques and the implementation of reference-frame-theory-based fault diagnosis for hybrid vehicles.

Fault Modeling, Diagnosis, and Implementation in One Volume

Based on years of research and development at the Electrical Machines & Power Electronics (EMPE) Laboratory at Texas A&M University, this book describes practical analysis and implementation strategies that readers can use in their work. It brings together, in one volume, the fundamentals of motor fault conditions, advanced fault modeling theory, fault diagnosis techniques, and low-cost DSP-based fault diagnosis implementation strategies.

Author Notes

Prof. Toliyat is currently a Raytheon Company endowed professor of electrical and computer engineering at Texas A&M University. He has received several awards, including the prestigious Cyrill Veinott Award in Electromechanical Energy Conversion from the IEEE Power Engineering Society (2004), the Patent and Innovation Award from Texas A&M University System Office of Technology Commercialization (2007), the TEES Faculty Fellow Award (2006), the Texas A&M Select Young Investigator Award (1999), and the Space Act Award from NASA (1999). He has also received four prize paper awards from the IEEE. Prof. Toliyat has published more than 370 technical papers (including more than 110 in IEEE Transactions) and has 12 issued and pending U.S. patents.

Seungdeog ChoiBilal Akin and Mina M. RahimianSubhasis NandiHomayoun Meshgin-KelkBashir Mahdi EbrahimiSubhasis NandiSubhasis NandiMasoud HajiaghajaniSeungdeog Choi and Bilal AkinBilal AkinSeungdeog Choi

Preface	p. xi
1 Introduction	p. 1
References	p. 8
2 Faults in Induction and Synchronous Motors	p. 9
2.1 Introduction of Induction Motor Fault	p. 9
2.1.1 Bearing Faults	p. 9
2.1.2 Stator Faults	p. 11
2.1.3 Broken Rotor Bar Fault	p. 13
2.1.4 Eccentricity Fault	p. 15
2.2 Introduction of Synchronous Motor Fault Diagnosis	p. 16
2.2.1 Damper Winding Fault	p. 17
2.2.2 Demagnetization Fault in Permanent Magnet Synchronous Machines (PMSMs)	p. 18
2.2.3 Eccentricity Fault	p. 19
2.2.4 Stator Inter-Turn Fault	p. 20
2.2.5 Rotor Inter-Turn Fault	p. 21
2.2.6 Bearing Fault	p. 22
References	p. 23
3 Modeling of Electric Machines Using Winding and Modified Winding Function Approaches	p. 27
3.1 Introduction	p. 27
3.2 Winding and Modified Winding Function Approaches (WFA and MWFA)	p. 28
3.3 Inductance Calculations Using WFA and MWFA	p. 33
3.4 Validation of Inductance Calculations Using WFA and MWFA	p. 39
References	p. 45
4 Modeling of Electric Machines Using Magnetic Equivalent Circuit Method	p. 47
4.1 Introduction	p. 47
4.2 Indirect Application of Magnetic Equivalent Circuit for Analysis of Salient Pole Synchronous Machines	p. 52
4.2.1 Magnetic Equivalent Circuit of a Salient Pole Synchronous Machine	p. 53
4.2.2 Inductance Relations of a Salient Pole Synchronous Machine	p. 55
4.2.3 Calculation of Inductances for a Salient Pole Synchronous Machine	p. 58
4.2.4 Experimental Measurement of Inductances of a Salient Pole Synchronous Machine	p. 63
4.3 Indirect Application of Magnetic Equivalent Circuit for Analysis of Induction Machines	p. 66
4.3.1 A Simplified Magnetic Equivalent Circuit of Induction Machines	p. 66
4.3.2 Inductance Relations of Induction Machines	p. 68
4.3.3 Calculation of Inductance of an Induction Machine	p. 70
4.4 Direct Application of Magnetic Equivalent Circuit Considering Nonlinear Magnetic Characteristic for Machine Analysis	p. 73
Appendix A Induction Machine Parameters	p. 77
Appendix B Node Permeance Matrices	p. 78
References	p. 79
5 Analysis of Faulty Induction Motors Using Finite Element Method	p. 81
5.1 Introduction	p. 81
5.2 Geometrical Modeling of Faulty Induction Motors Using Time-Stepping Finite Element Method (TSFEM)	p. 82
5.3 Coupling of Electrical Circuits and Finite Element Area	p. 83
5.4 Modeling Internal Faults Using Finite Element Method	p. 85
5.4.1 Modeling Broken Bar Fault	p. 85
5.4.2 Modeling Eccentricity Fault	p. 87
5.4.2.1 Static Eccentricity	p. 87
5.4.2.2 Dynamic Eccentricity	p. 89
5.4.2.3 Mixed Eccentricity	p. 90
5.5 Impact of Magnetic Saturation on Accurate Fault Detection in Induction Motors	p. 91
5.5.1 Analysis of Air-Gap Magnetic Flux Density in Healthy and Faulty Induction Motor	p. 94
5.5.1.1 Linear Magnetization Characteristic	p. 94
5.5.1.2 Nonlinear Magnetization Characteristic	p. 95
References	p. 96
6 Fault Diagnosis of Electric Machines Using Techniques Based on Frequency Domain	p. 99
6.1 Introduction	p. 99
6.2 Some Definitions and Examples Related to Signal Processing	p. 100
6.2.1 Continuous versus Discrete or Digital or Sampled Signal	p. 100
6.2.2 Continuous, Discrete Fourier Transforms, and Nonparametric Power Spectrum Estimation	p. 101
6.2.3 Parametric Power Spectrum Estimation	p. 105
6.2.4 Power Spectrum Estimation Using Higher-Order Spectra (HOS)	p. 107
6.2.5 Power Spectrum Estimation Using Swept Sine Measurements or Digital Frequency Locked Loop Technique (DFLL)	p. 110
6.3 Diagnosis of Machine Faults Using Frequency-Domain-Based Techniques	p. 111
6.3.1 Detection of Motor Bearing Faults	p. 111
6.3.1.1 Mechanical Vibration Frequency Analysis to Detect Bearing Faults	p. 111
6.3.1.2 Line Current Frequency Analysis to Detect Bearing Faults	p. 115
6.3.2 Detection of Stator Faults	p. 116
6.3.2.1 Detection of Stator Faults Using External Flux Sensors	p. 116
6.3.2.2 Detection of Stator Faults Using Line Current Harmonics	p. 117
6.3.2.3 Detection of Stator Faults Using Terminal Voltage Harmonics at Switch-Off	p. 119
6.3.2.4 Detection of Stator Faults Using Field Current and Rotor Search Coil Harmonics in Synchronous Machines	p. 121
6.3.2.5 Detection of Stator Faults Using Rotor Current and Search Coil Voltages Harmonics in Wound Rotor Induction Machines	p. 124
6.3.3 Detection of Rotor Faults	p. 129
6.3.3.1 Detection of Rotor Faults in Stator Line Current, Speed, Torque, and Power	p. 130
6.3.3.2 Detection of Rotor Faults in External and Internal Search Coil	p. 134
6.3.3.3 Detection of Rotor Faults Using Terminal Voltage Harmonics at Switch-Off	p. 134
6.3.3.4 Detection of Rotor Faults at Start-Up	p. 134
6.3.3.5 Detection of Rotor Faults in Presence of Interbar Current Using Axial Vibration Signals	p. 135
6.3.4 Detection of Eccentricity Faults	p. 136
6.3.4.1 Detection of Eccentricity Faults Using Line Current Signal Spectra	p. 136
6.3.4.2 Detection of Eccentricity Faults Based on Nameplate Parameters	p. 142
6.3.4.3 Detection of Eccentricity Faults Using Mechanical Vibration Signal Spectra	p. 147
6.3.4.4 Detection of Inclined Eccentricity Faults	p. 147
6.3.5 Detection of Faults in Inverter-Fed Induction Machines	p. 148
References	p. 149
7 Fault Diagnosis of Electric Machines Using Model-Based Techniques	p. 155
7.1 Introduction	p. 155
7.2 Model of Healthy Three-Phase Squirrel-Cage Induction Motor	p. 158
7.3 Model of Three-Phase Squirrel-Cage Induction Motor with Stator Inter-Turn Faults	p. 165
7.3.1 Model without Saturation	p. 165
7.3.2 Model with Saturation	p. 169
7.4 Model of Squirrel-Cage Induction Motor with Incipient Broken Rotor Bar and End-Ring Faults	p. 175
7.5 Model of Squirrel-Cage Induction Motors with Eccentricity Faults	p. 177
7.6 Model of a Synchronous Reluctance Motor with Stator Fault	p. 179
7.7 Model of a Salient Pole Synchronous Motor with Dynamic Eccentricity Faults	p. 181
References	p. 183
8 Application of Pattern Recognition to Fault Diagnosis	p. 185
8.1 Introduction	p. 185
8.2 Bayesian Theory and Classifier Design	p. 186
8.3 Simplified Form for a Normal Distribution	p. 189
8.4 Feature Extraction for Our Fault Diagnosis System	p. 190
8.5 Classifier Training	p. 192
8.6 Implementation	p. 194
References	p. 198
9 Implementation of Motor Current Signature Analysis Fault Diagnosis Based on Digital Signal Processors	p. 199
9.1 Introduction	p. 199
9.1.1 Cross-Correlation Scheme Derived from Optimal Detector in Additive White Gaussian Noise (AWGN) Channel	p. 200
9.2 Reference Frame Theory	p. 201
9.2.1 Reference Frame Theory for Condition Monitoring	p. 202
9.2.2 (Fault) Harmonic Analysis of Multiphase Systems	p. 202
9.2.3 On-Line Fault Detection Results	p. 204
9.2.3.1 v/f Controlled Inverter-Fed Motor Line Current Analysis	p. 204
9.2.3.2 Field-Oriented Control Inverter-Fed Motor Line Current Analysis	p. 206
9.2.3.3 Instantaneous Fault Monitoring in Time-Frequency Domain and Transient Analysis	p. 206
9.3 Phase-Sensitive Detection-Based Fault Diagnosis	p. 210
9.3.1 Introduction	p. 210
9.3.2 Phase-Sensitive Detection	p. 210
9.3.3 On-Line Experimental Results	p. 212
References	p. 218
10 Implementation of Fault Diagnosis in Hybrid Electric Vehicles Based on Reference Frame Theory	p. 221
10.1 Introduction	p. 221
10.2 On-Board Fault Diagnosis (OBD) for Hybrid Electric Vehicles (HEVs)	p. 221
10.3 Drive Cycle Analysis for OBD	p. 224
10.4 Rotor Asymmetry Detection at Zero Speed	p. 226
References	p. 233
11 Robust Signal Processing Techniques, for the Implementation of Motor Current Signature Analysis Diagnosis Based on Digital Signal Processors	p. 235
11.1 Introduction	p. 235
11.1.1 Coherent Detection	p. 236
11.1.2 Noncoherent Detection (Phase Ambiguity-Compensation)	p. 237
11.1.3 Fault Frequency Offset Compensation	p. 237
11.2 Decision-Making Scheme	p. 240
11.2.1 Adaptive Threshold Design (Noise Ambiguity Compensation)	p. 240
11.2.2 Q-Function	p. 242
11.2.3 Noise Estimation	p. 243
11.3 Simulation and Experimental Result	p. 244
11.3.1 Modeled MATLAB Simulation Result	p. 244
11.3.2 Off-Line Experiments	p. 245
11.3.2.1 Off-Line Results for Eccentricity	p. 246
11.3.2.2 Off-Line Results for Broken Rotor Bar	p. 247
11.3.3 On-Line Experimental Results	p. 248
References	p. 251
Index	p. 253

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