Electrical Systems 2

Abdenour Soualhi is an Assistant Professor in Electrical and Mechanical Engineering at LASPI Laboratory, Jean Monnet University, Roanne, France. His main research interests are in signal processing, diagnosis and prognosis of faults. Hubert Razik is a Full Professor of Electrical Engineering at Claude Bernard University Lyon I, France. His main research interests include modeling, control and monitoring conditions of multiphase induction motors.

Introduction ix

Chapter 1. Diagnosis of Electrical Machines by External Field Measurement 1
Remus PUSCA, Eric LEFEVRE, David MERCIER, Raphael ROMARY and Miftah IRHOUMAH

1.1. Introduction 1

1.2. Extracting indicators from the external magnetic field 3

1.2.1. External field classification 3

1.2.2. Attenuation of the transverse field 5

1.2.3. Measurement of the transverse field 6

1.2.4. Modeling a healthy machine 8

1.2.5. Modeling a faulty machine 10

1.2.6. Effect of the load 13

1.3. Information fusion to detect the inter-turn short-circuit faults 16

1.3.1. Belief function theory: basic concepts 17

1.3.2. Fault detection with the fusion method 19

1.3.3. Calculation example 21

1.4. Application 25

1.4.1. Presentation of rotating electrical machines 25

1.4.2. Presentation of experimental results 28

1.5. Conclusion 33

1.6. References 33

Chapter 2. Signal Processing Techniques for Transient Fault Diagnosis 37
José Alfonso Antonino DAVIU and Roque Alfredo Osornio RIOS

2.1. Introduction 37

2.2. Fault detection via motor current analysis 41

2.2.1. Classical tools (MCSA) 41

2.2.2. New techniques based on transient analysis (ATCSA) 45

2.3. Signal processing tools for transient analysis 47

2.3.1. Example of a discrete tool: the DWT 48

2.3.2. Example of a continuous tool: the HHT 54

2.4. Application of transient-based tools for electric motor fault detection 67

2.4.1. Application of the DWT for the detection of rotor damage 68

2.4.2. Application of the HHT for the detection of rotor damage 70

2.5. Conclusions 71

2.6. References 72

Chapter 3. Accurate Stator Fault Detection in an Induction Motor Using the Symmetrical Current Components 77
Monia BOUZID and Gérard CHAMPENOIS

3.1. Introduction 77

3.2. Study of the SCCs behavior in an IM under different stator faults 79

3.2.1. Simulation study 79

3.2.2. Analytical study of the SCCs in an IM under different stator faults 86

3.3. Extracting stator fault indicators from an IM 97

3.4. Automatic and accurate detection and diagnosis of stator faults 98

3.4.1. Description of the monitoring system of the IM operating state 98

3.4.2. Improving the accuracy of incipient stator fault detection 99

3.4.3. Automatic incipient stator fault diagnosis in an IM 114

3.5. Conclusion 118

3.6. References 119

Chapter 4. Bearing Fault Diagnosis in Rotating Machines 123
Claude DELPHA, Demba DIALLO, Jinane HARMOUCHE, Mohamed BENBOUZID, Yassine AMIRAT and Elhoussin ELBOUCHIKHI

4.1. Introduction 124

4.1.1. Bearing fault detection and diagnosis overview 124

4.1.2. Problem statement and proposal 128

4.2. Method description 130

4.2.1. The global spectral analysis description 130

4.2.2. Discrimination of faults in the bearing balls using LDA 133

4.3. Experimental data 135

4.3.1. Experimental test bed description 135

4.3.2. Time-domain detection 137

4.4. Global spectra bearing diagnosis 139

4.4.1. Data preprocessing 139

4.4.2. Global spectra results with PCA 141

4.4.3. Global spectra results with LDA 143

4.5. Conclusion 146

4.6. References 147

Chapter 5. Diagnosis and Prognosis of Proton Exchange Membrane Fuel Cells 153
Zhongliang LI, Zhixue ZHENG and Fei GAO

5.1. Introduction 153

5.2. PEMFC functioning principle and development status 154

5.2.1. From a PEMFC to a PEMFC system 154

5.2.2. Current status of the PEMFC technology 156

5.3. Faults and degradation of PEMFCs 157

5.3.1. Degradation related to the aging effects 157

5.3.2. Degradation related to system operations 158

5.3.3. Variables used for PEMFC degradation evaluation 161

5.4. PEMFC diagnostic methods 165

5.4.1. Model-based diagnostic methods 165

5.4.2. Data-driven diagnostic methods 168

5.4.3. Case study 171

5.5. Prognosis of PEMFCs 180

5.5.1. Health index and EoL 181

5.5.2. Model-based prognostic methods 182

5.5.3. Data-driven and hybrid prognostic methods 184

5.5.4. Case study 186

5.6. Remaining challenges 193

5.7. References 194

List of Authors 199

Index 201

Summary of Volume 1 203