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Electrical Machines Diagnosis

  • ID: 2182832
  • Book
  • 350 Pages
  • John Wiley and Sons Ltd
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Monitoring and diagnosis of electrical machine faults is a scientific and economic issue which is motivated by objectives for reliability and serviceability in electrical drives.

This book provides a survey of the techniques used to detect the faults occurring in electrical drives: electrical, thermal and mechanical faults of the electrical machine, faults of the static converter and faults of the energy storage unit.

Diagnosis of faults occurring in electrical drives is an essential part of a global monitoring system used to improve reliability and serviceability. This diagnosis is performed with a large variety of techniques: parameter estimation, state observation, Kalman filtering, spectral analysis, neural networks, fuzzy logic, artificial intelligence, etc. Particular emphasis in this book is put on the modeling of the electrical machine in faulty situations.

Electrical Machines Diagnosis presents original results obtained mainly by French researchers in different domains. It will be useful as a guideline for the conception of more robust electrical machines and indeed for engineers who have to monitor and maintain electrical drives. As the monitoring and diagnosis of electrical machines is still an open domain, this book will also be very useful to researchers.
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Preface xi

Chapter 1. Faults in Electrical Machines and their Diagnosis 1
Sadok BAZINE and Jean–Claude TRIGEASSOU

1.1. Introduction 1

1.2. Composition of induction machines 3

1.3. Failures in induction machines 5

1.4. Overview of methods for diagnosing induction machines 10

1.5. Conclusion 18

1.6. Bibliography 19

Chapter 2. Modeling Induction Machine Winding Faults for Diagnosis 23
Emmanuel SCHAEFFER and Smail BACHIR

2.1. Introduction 23

2.2. Study framework and general methodology 26

2.3. Model of the machine with a stator insulation fault 40

2.4. Generalization of the approach to the coupled modeling of stator and rotor faults 51

2.5. Methodology for monitoring the induction machine 57

2.6. Conclusion 64

2.7. Bibliography 67

Chapter 3. Closed–Loop Diagnosis of the Induction Machine 69

3.1. Introduction 69

3.2. Closed–loop identification 71

3.3. General methodology of closed–loop identification of induction machine 74

3.4. Closed–loop diagnosis of simultaneous stator/rotor faults 82

3.5. Conclusion 89

3.6. Bibliography 90

Chapter 4. Induction Machine Diagnosis Using Observers 93
Guy CLERC and Jean–Claude MARQUES

4.1. Introduction 93

4.2. Model presentation 96

4.3. Observers 104

4.4. Applying observers to diagnostics 119

4.5. Conclusion 127

4.6. Bibliography 128

Chapter 5. Thermal Monitoring of the Induction Machine 131
Luc LORON and Emmanuel FOULON

5.1. Introduction 131

5.2. Real–time parametric estimation by Kalman filter 137

5.3. Electrical models for the thermal monitoring 142

5.4. Experimental system 149

5.5. Experimental results 157

5.6. Conclusion 162

5.7. Appendix: induction machine characteristics 163

5.8. Bibliography 163

Chapter 6. Diagnosis of the Internal Resistance of an Automotive Lead–acid Battery by the Implementation of a Model Invalidation–based Approach: Application to Crankability Estimation 167
Jocelyn SABATIER, Mikaël CUGNET, Stéphane LARUELLE, Sylvie GRUGEON, Isabelle CHANTEUR, Bernard SAHUT,

6.1. Introduction 167

6.2. Fractional model of a lead–acid battery for the start–up phase 169

6.3. Identification of the fractional model 171

6.4. Battery resistance as crankability estimator 175

6.5. Model validation and estimation of the battery resistance 178

6.6. Toward a battery state estimator 188

6.7. Conclusion 188

6.8. Bibliography 190

Chapter 7. Electrical and Mechanical Faults Diagnosis of Induction Machines using Signal Analysis 193

7.1. Introduction 193

7.2. The spectrum of the current line 194

7.3. Signal processing 196

7.4. Signal analysis from experiment campaigns 199

7.5. Conclusion 222

7.6. Appendices 223

7.7. Bibliography 224

Chapter 8. Fault Diagnosis of the Induction Machine by Neural Networks 227
Monia Ben Khader BOUZID, Najiba MRABET BELLAAJ, Khaled JELASSI, Gérard CHAMPENOIS and Sandrine MOREAU

8.1. Introduction 227

8.2. Methodology of the use of the ANN in the diagnostic domain 228

8.3. Description of the monitoring system 232

8.4. The detection problem 233

8.5. The proposed method for the robust detection 235

8.6. Signature of the stator and rotor faults 237

8.7. Detection of the faults by the RNd neural network 244

8.8. Diagnosis of the stator fault 251

8.9. Diagnosis of the rotor fault 263

8.10. Complete monitoring system of the induction machine 267

8.11. Conclusion 268

8.12. Bibliography 269

Chapter 9. Faults Detection and Diagnosis in a Static Converter 271
Mohamed BENBOUZID, Claude DELPHA, Zoubir KHATIR, Stéphane LEFEBVRE and Demba DIALLO

9.1. Introduction 271

9.2. Detection and diagnosis 273

9.3. Thermal fatigue of power electronic moduli and failure modes 294

9.4. Conclusion 316

9.5. Bibliography 316

List of Authors 321

Index 327

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Jean–Claude Trigeassou
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