Jean-Claude Trigeassou was Professor at ESIP, an engineering school at Poitiers University, from 1988 to 2006. His major research interests are in the method of moments with applications to identification and control and in the parameter estimation of continuous systems with application to the diagnosis of electrical machines. At present, he is associated with the activities of the IMS-LAPS at Bordeaux University and his research works deal with modeling, stability, identification and control of fractional order systems.

• Preface xiChapter 1. Faults in Electrical Machines and their Diagnosis 1Sadok BAZINE and Jean-Claude TRIGEASSOU1.1. Introduction 11.2. Composition of induction machines 31.3. Failures in induction machines 51.4. Overview of methods for diagnosing induction machines 101.5. Conclusion 181.6. Bibliography 19Chapter 2. Modeling Induction Machine Winding Faults for Diagnosis 23Emmanuel SCHAEFFER and Smail BACHIR2.1. Introduction 232.2. Study framework and general methodology 262.3. Model of the machine with a stator insulation fault 402.4. Generalization of the approach to the coupled modeling of stator and rotor faults 512.5. Methodology for monitoring the induction machine 572.6. Conclusion 642.7. Bibliography 67Chapter 3. Closed-Loop Diagnosis of the Induction Machine 69Imène BEN AMEUR BAZINE, Jean-Claude TRIGEASSOU, Khaled JELASSI and Thierry POINOT3.1. Introduction 693.2. Closed-loop identification 713.3. General methodology of closed-loop identification of induction machine 743.4. Closed-loop diagnosis of simultaneous stator/rotor faults 823.5. Conclusion 893.6. Bibliography 90Chapter 4. Induction Machine Diagnosis Using Observers 93Guy CLERC and Jean-Claude MARQUES4.1. Introduction 934.2. Model presentation 964.3. Observers 1044.4. Applying observers to diagnostics 1194.5. Conclusion 1274.6. Bibliography 128Chapter 5. Thermal Monitoring of the Induction Machine 131Luc LORON and Emmanuel FOULON5.1. Introduction 1315.2. Real-time parametric estimation by Kalman filter 1375.3. Electrical models for the thermal monitoring 1425.4. Experimental system 1495.5. Experimental results 1575.6. Conclusion 1625.7. Appendix: induction machine characteristics 1635.8. Bibliography 163Chapter 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 167Jocelyn SABATIER, Mikaël CUGNET, Stéphane LARUELLE, Sylvie GRUGEON, Isabelle CHANTEUR, Bernard SAHUT,Alain OUSTALOUP and Jean-Marie TARASCON6.1. Introduction 1676.2. Fractional model of a lead-acid battery for the start-up phase 1696.3. Identification of the fractional model 1716.4. Battery resistance as crankability estimator 1756.5. Model validation and estimation of the battery resistance 1786.6. Toward a battery state estimator 1886.7. Conclusion 1886.8. Bibliography 190Chapter 7. Electrical and Mechanical Faults Diagnosis of Induction Machines using Signal Analysis 193Hubert RAZIK and Mohamed EL KAMEL OUMAAMAR7.1. Introduction 1937.2. The spectrum of the current line 1947.3. Signal processing 1967.4. Signal analysis from experiment campaigns 1997.5. Conclusion 2227.6. Appendices 2237.7. Bibliography 224Chapter 8. Fault Diagnosis of the Induction Machine by Neural Networks 227Monia Ben Khader BOUZID, Najiba MRABET BELLAAJ, Khaled JELASSI, Gérard CHAMPENOIS and Sandrine MOREAU8.1. Introduction 2278.2. Methodology of the use of the ANN in the diagnostic domain 2288.3. Description of the monitoring system 2328.4. The detection problem 2338.5. The proposed method for the robust detection 2358.6. Signature of the stator and rotor faults 2378.7. Detection of the faults by the RNd neural network 2448.8. Diagnosis of the stator fault 2518.9. Diagnosis of the rotor fault 2638.10. Complete monitoring system of the induction machine 2678.11. Conclusion 2688.12. Bibliography 269Chapter 9. Faults Detection and Diagnosis in a Static Converter 271Mohamed BENBOUZID, Claude DELPHA, Zoubir KHATIR, Stéphane LEFEBVRE and Demba DIALLO9.1. Introduction 2719.2. Detection and diagnosis 2739.3. Thermal fatigue of power electronic moduli and failure modes 2949.4. Conclusion 3169.5. Bibliography 316List of Authors 321Index 327

Jean-Claude Trigeassou was Professor at ESIP, an engineering school at Poitiers University, from 1988 to 2006. His major research interests are in the method of moments with applications to identification and control and in the parameter estimation of continuous systems with application to the diagnosis of electrical machines. At present, he is associated with the activities of the IMS-LAPS at Bordeaux University and his research works deal with modeling, stability, identification and control of fractional order systems.

• Preface xiChapter 1. Faults in Electrical Machines and their Diagnosis 1Sadok BAZINE and Jean-Claude TRIGEASSOU1.1. Introduction 11.2. Composition of induction machines 31.3. Failures in induction machines 51.4. Overview of methods for diagnosing induction machines 101.5. Conclusion 181.6. Bibliography 19Chapter 2. Modeling Induction Machine Winding Faults for Diagnosis 23Emmanuel SCHAEFFER and Smail BACHIR2.1. Introduction 232.2. Study framework and general methodology 262.3. Model of the machine with a stator insulation fault 402.4. Generalization of the approach to the coupled modeling of stator and rotor faults 512.5. Methodology for monitoring the induction machine 572.6. Conclusion 642.7. Bibliography 67Chapter 3. Closed-Loop Diagnosis of the Induction Machine 69Imène BEN AMEUR BAZINE, Jean-Claude TRIGEASSOU, Khaled JELASSI and Thierry POINOT3.1. Introduction 693.2. Closed-loop identification 713.3. General methodology of closed-loop identification of induction machine 743.4. Closed-loop diagnosis of simultaneous stator/rotor faults 823.5. Conclusion 893.6. Bibliography 90Chapter 4. Induction Machine Diagnosis Using Observers 93Guy CLERC and Jean-Claude MARQUES4.1. Introduction 934.2. Model presentation 964.3. Observers 1044.4. Applying observers to diagnostics 1194.5. Conclusion 1274.6. Bibliography 128Chapter 5. Thermal Monitoring of the Induction Machine 131Luc LORON and Emmanuel FOULON5.1. Introduction 1315.2. Real-time parametric estimation by Kalman filter 1375.3. Electrical models for the thermal monitoring 1425.4. Experimental system 1495.5. Experimental results 1575.6. Conclusion 1625.7. Appendix: induction machine characteristics 1635.8. Bibliography 163Chapter 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 167Jocelyn SABATIER, Mikaël CUGNET, Stéphane LARUELLE, Sylvie GRUGEON, Isabelle CHANTEUR, Bernard SAHUT,Alain OUSTALOUP and Jean-Marie TARASCON6.1. Introduction 1676.2. Fractional model of a lead-acid battery for the start-up phase 1696.3. Identification of the fractional model 1716.4. Battery resistance as crankability estimator 1756.5. Model validation and estimation of the battery resistance 1786.6. Toward a battery state estimator 1886.7. Conclusion 1886.8. Bibliography 190Chapter 7. Electrical and Mechanical Faults Diagnosis of Induction Machines using Signal Analysis 193Hubert RAZIK and Mohamed EL KAMEL OUMAAMAR7.1. Introduction 1937.2. The spectrum of the current line 1947.3. Signal processing 1967.4. Signal analysis from experiment campaigns 1997.5. Conclusion 2227.6. Appendices 2237.7. Bibliography 224Chapter 8. Fault Diagnosis of the Induction Machine by Neural Networks 227Monia Ben Khader BOUZID, Najiba MRABET BELLAAJ, Khaled JELASSI, Gérard CHAMPENOIS and Sandrine MOREAU8.1. Introduction 2278.2. Methodology of the use of the ANN in the diagnostic domain 2288.3. Description of the monitoring system 2328.4. The detection problem 2338.5. The proposed method for the robust detection 2358.6. Signature of the stator and rotor faults 2378.7. Detection of the faults by the RNd neural network 2448.8. Diagnosis of the stator fault 2518.9. Diagnosis of the rotor fault 2638.10. Complete monitoring system of the induction machine 2678.11. Conclusion 2688.12. Bibliography 269Chapter 9. Faults Detection and Diagnosis in a Static Converter 271Mohamed BENBOUZID, Claude DELPHA, Zoubir KHATIR, Stéphane LEFEBVRE and Demba DIALLO9.1. Introduction 2719.2. Detection and diagnosis 2739.3. Thermal fatigue of power electronic moduli and failure modes 2949.4. Conclusion 3169.5. Bibliography 316List of Authors 321Index 327