Electrical Machines Diagnosis Electrical Machines Diagnosis Edited by Jean-Claude Trigeassou First published 2011 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address: ISTE Ltd 27-37 St George’s Road London SW19 4EU UK John Wiley & Sons, Inc 111 River Street Hoboken, NJ 07030 USA www.iste.co.uk www.wiley.com © ISTE Ltd 2011 The rights of Jean-Claude Trigeassou to be identified as the author of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988 Library of Congress Cataloging-in-Publication Data Electrical machines diagnosis / edited by Jean-Claude Trigeassou p cm Includes bibliographical references and index ISBN 978-1-84821-263-3 Electric apparatus and appliances Maintenance and repair Electric machinery Maintenance and repair Electric fault location I Trigeassou, Jean-Claude TK452.E4155 2011 621.31'0420288 dc23 2011022945 British Library Cataloguing-in-Publication Data A CIP record for this book is available from the British Library ISBN 978-1-84821-263-3 Printed and bound in Great Britain by CPI Antony Rowe, Chippenham and Eastbourne Table of Contents Preface xi Chapter Faults in Electrical Machines and their Diagnosis Sadok BAZINE and Jean-Claude TRIGEASSOU 1.1 Introduction 1.2 Composition of induction machines 1.2.1 The stator 1.2.2 The rotor 1.2.3 Bearings 1.3 Failures in induction machines 1.3.1 Mechanical failures 1.3.2 Electrical failures 1.4 Overview of methods for diagnosing induction machines 1.4.1 Diagnosis methods using an analytical model 1.4.2 Diagnostic methods with no analytical model 1.5 Conclusion 1.6 Bibliography 4 5 10 12 16 18 19 Chapter Modeling Induction Machine Winding Faults for Diagnosis Emmanuel SCHAEFFER and Smail BACHIR 23 2.1 Introduction 2.1.1 Simulation model versus diagnosis model 2.1.2 Objectives 2.1.3 Methodology 2.1.4 Chapter structure 2.2 Study framework and general methodology 2.2.1 Working hypotheses 23 23 24 24 25 26 26 vi Electrical Machines Diagnosis 2.2.2 Equivalence between winding systems 2.2.3 Equivalent two-phase machine with no fault 2.2.4 Consideration of a stator winding fault 2.3 Model of the machine with a stator insulation fault 2.3.1 Electrical equations of the machine with a stator short-circuit 2.3.2 State model in any reference frame 2.3.3 Extension of the three-phase stator model 2.3.4 Model validation 2.4 Generalization of the approach to the coupled modeling of stator and rotor faults 2.4.1 Electrical equations in the presence of rotor imbalance 2.4.2 Generalized model of the machine with stator and rotor faults 2.5 Methodology for monitoring the induction machine 2.5.1 Parameter estimation for induction machine diagnosis 2.5.2 Experimental validation of the monitoring strategy 2.6 Conclusion 2.7 Bibliography 27 34 37 40 40 43 47 48 51 53 55 57 58 61 64 67 Chapter Closed-Loop Diagnosis of the Induction Machine Imène BEN AMEUR BAZINE, Jean-Claude TRIGEASSOU, Khaled JELASSI and Thierry POINOT 69 3.1 Introduction 3.2 Closed-loop identification 3.2.1 Problems in closed-loop identification 3.2.2 Identification problems for diagnosing electrical machines 3.3 General methodology of closed-loop identification of induction machine 3.3.1 Taking control into account 3.3.2 Machine identification by closed-loop decomposition 3.3.3 Identification results 3.4 Closed-loop diagnosis of simultaneous stator/rotor faults 3.4.1 General model of induction machine faults 3.4.2 Parameter estimation with a priori information 3.4.3 Detection and localization 3.4.4 Comparison of identification results through direct and indirect approaches 3.5 Conclusion 3.6 Bibliography 69 71 71 73 74 74 76 80 82 82 83 84 87 89 90 Table of Contents Chapter Induction Machine Diagnosis Using Observers Guy CLERC and Jean-Claude MARQUES 4.1 Introduction 4.2 Model presentation 4.2.1 Three-phase model of induction machine without fault 4.2.2 Park’s model of an induction machine without fault 4.2.3 Induction machine models with fault 4.3 Observers 4.3.1 Principle 4.3.2 Different kinds of observers 4.3.3 Extended observer 4.4 Applying observers to diagnostics 4.4.1 Using Park’s model 4.4.2 Use of the three-phase model 4.4.3 Spectral analysis of the torque reconstructed by the observer 4.5 Conclusion 4.6 Bibliography 93 93 96 96 100 104 104 104 108 115 119 119 124 125 127 128 Chapter Thermal Monitoring of the Induction Machine Luc LORON and Emmanuel FOULON 131 5.1 Introduction 5.1.1 Aims of the thermal monitoring on induction machines 5.1.2 Main methods of thermal monitoring of the induction machines 5.2 Real-time parametric estimation by Kalman filter 5.2.1 Interest and specificities of the Kalman filter 5.2.2 Implementation of an extended Kalman filter 5.3 Electrical models for the thermal monitoring 5.3.1 Continuous time models 5.3.2 Full-order model 5.3.3 Discretized and extended model 5.4 Experimental system 5.4.1 General presentation of the test bench 5.4.2 Thermal instrumentation 5.4.3 Electrical instrumentation 5.5 Experimental results 5.5.1 Tuning of the Kalman filter 5.5.2 Influence of the magnetic saturation 5.6 Conclusion 5.7 Appendix: induction machine characteristics 5.8 Bibliography vii 131 131 133 137 137 138 142 143 144 147 149 149 151 153 157 157 160 162 163 163 viii Electrical Machines Diagnosis Chapter Diagnosis of the Internal Resistance of an Automotive Lead-acid Battery by the Implementation of a Model Invalidation-based Approach: Application to Crankability Estimation Jocelyn SABATIER, Mikaël CUGNET, Stéphane LARUELLE, Sylvie GRUGEON, Isabelle CHANTEUR, Bernard SAHUT, Alain OUSTALOUP and Jean-Marie TARASCON 6.1 Introduction 6.2 Fractional model of a lead-acid battery for the start-up phase 6.3 Identification of the fractional model 6.3.1 Output error identification algorithm 6.3.2 Calculation of the output sensitivities 6.3.3 Validation of the estimated parameters 6.3.4 Application to start-up signals 6.4 Battery resistance as crankability estimator 6.5 Model validation and estimation of the battery resistance 6.5.1 Frequency approach of the model validation 6.5.2 Application to the estimation of the battery resistance 6.5.3 Simplified resistance estimator 6.6 Toward a battery state estimator 6.7 Conclusion 6.8 Bibliography 167 169 171 171 173 174 174 175 178 178 181 184 188 188 190 Chapter Electrical and Mechanical Faults Diagnosis of Induction Machines using Signal Analysis Hubert RAZIK and Mohamed EL KAMEL OUMAAMAR 193 7.1 Introduction 7.2 The spectrum of the current line 7.3 Signal processing 7.3.1 Fourier’s transform 7.3.2 Periodogram 7.4 Signal analysis from experiment campaigns 7.4.1 Disturbances induced by a broken bar 7.4.2 Bearing faults 7.4.3 Static eccentricity 7.4.4 Inter turn short circuits 7.5 Conclusion 7.6 Appendices 7.6.1 Appendix A 7.6.2 Appendix B 7.7 Bibliography 167 193 194 196 196 197 199 199 205 211 220 222 223 223 223 224 Table of Contents Chapter Fault Diagnosis of the Induction Machine by Neural Networks Monia Ben Khader BOUZID, Najiba MRABET BELLAAJ, Khaled JELASSI, Gérard CHAMPENOIS and Sandrine MOREAU 8.1 Introduction 8.2 Methodology of the use of the ANN in the diagnostic domain 8.2.1 Choice of the fault indicators 8.2.2 Choice of the structure of the network 8.2.3 Construction of the learning and test base 8.2.4 Learning and test of the network 8.3 Description of the monitoring system 8.4 The detection problem 8.5 The proposed method for the robust detection 8.5.1 Generation of the estimated residues 8.6 Signature of the stator and rotor faults 8.6.1 Analysis of the residue in healthy regime 8.6.2 Analysis of the residue in presence of the stator fault 8.6.3 Analysis of the residue in presence of the rotor fault 8.6.4 Analysis of the residue in presence of simultaneous stator/rotor fault 8.7 Detection of the faults by the RNd neural network 8.7.1 Extraction of the fault indicators 8.7.2 Learning sequence of the RNd network 8.7.3 Structure of the RNd network 8.7.4 Results of the learning of the RNd network 8.7.5 Test results of the RNd network 8.8 Diagnosis of the stator fault 8.8.1 Choice of the fault indicators for the RNcc network 8.8.2 Learning sequence of the RNcc network 8.8.3 Structure of the RNcc network 8.8.4 Learning results of the RNcc network 8.8.5 Results of the test of the RNcc network 8.8.6 Experimental validation of the RNcc network 8.9 Diagnosis of the rotor fault 8.9.1 Choice of the fault indicators of the RNbc network 8.9.2 Learning sequence of the RNbc network 8.9.3 Learning, test and validation results 8.10 Complete monitoring system of the induction machine 8.11 Conclusion 8.12 Bibliography ix 227 227 228 229 230 231 232 232 233 235 236 237 237 237 241 244 244 244 245 246 247 248 251 251 253 254 255 256 259 263 265 265 266 267 268 269 x Electrical Machines Diagnosis Chapter Faults Detection and Diagnosis in a Static Converter Mohamed BENBOUZID, Claude DELPHA, Zoubir KHATIR, Stéphane LEFEBVRE and Demba DIALLO 9.1 Introduction 9.2 Detection and diagnosis 9.2.1 Neural network approach 9.2.2 A fuzzy logic approach 9.2.3 Multi-dimensional data analysis 9.3 Thermal fatigue of power electronic moduli and failure modes 9.3.1 Presentation of power electronic moduli in diagnosis 9.3.2 Causes and main types of degradation of power electronics moduli 9.3.3 Interconnection degradation effects on electrical characteristics and potential use for diagnosis 9.3.4 Effects of interface degradation on thermal characteristics and potential use for diagnosis 9.4 Conclusion 9.5 Bibliography 271 271 273 273 280 285 294 294 304 310 313 316 316 List of Authors 321 Index 327 318 Electrical Machines Diagnosis [KHO 07a] KHOMFOI S., TOLBERT L.M., “Diagnosis and reconfiguration for multilevel inverter drive using AI-based techniques”, IEEE Transactions Industrial Electronics, vol 54, no 6, p 2954-2968, December 2007 [KHO 07b] KHONG B et al., “Characterization and modeling of aging failures on power MOSFET devices”, Microelectronics Reliability, vol 47, p 1745-1750, 2007 [LEF 03] LEFRANC G et al., “Aluminum bond-wire properties after billion mechanical cycles”, Microelectronics Reliability, vol 43, p 1833-1838, 2003 [LU 09] LU B., SHARMA S.K., “A literature review of IGBT fault diagnostic and protection methods for power inverters”, IEEE Transactions Industry Applications, vol 45, no 5, p 1770-177, September-October 2009 [MAN 00] MACA J.V., WONDRAK W et al., “High temperature time dependent dielectric breakdown of power MOSFETs”, HITEC Session V, 2000 [MIL 95] MILITARY HANDBOOK 217F, Reliability prediction of electronic equipment, 28 February 1995 [MIT 99] MITIC G., BEINERT R et al., “Reliability of AlN Substrates and their Solder Joints in IGBT Power Modules”, Microelectronics Reliability, vol 39, p 1159-1164, 1999 [MOH 09] MOHAGHEGHI S., HARLEY R.G., HABETLER T.G., DIVAN D., “Condition monitoring of power electronic circuits using artificial neural networks”, IEEE Transactions Power Electronics, vol 24, no 10, p 2363-2367, October 2009 [MOR 88] MORRISSON D.F., Multivariate Statistical Methods, p 415, McGraw-Hill, Singapore, 1988 [MUR 06] MURPHEY Y., MASRUR M.A., CHEN Z., ZHANG B., “Model-based fault diagnosis in electric drives using machine learning”, IEEE/ASME Transactions Mechatronics, vol 11, no 3, p 290-303, June 2006 [NAG 00] NAGATOMO Y., NAGASE T., “The study of the power modules with high reliability for EV use”, 17th EVS Conference, Montreal, October 2000 [NEJ 00] NEJJARI H., BENBOUZID M.E.H., “Monitoring and diagnosis of induction motors electrical faults using a current Park’s vector pattern learning approach”, IEEE Transactions Industry Applications, vol 36, no 3, p 730-735, May-June 2000 [OLD 04] OLDERVOLL F., STRISLAND F., “Wire-bond failure mechanisms in plastic encapsulated microcircuits and ceramic hybrids at high temperatures”, Microelectronics Reliability, vol 44, no 6, p 1009-1015, 2004 [OND 09] ONDEL O., CLERC G., BOUTLEUX E., BLANCO E., “Fault detection and diagnosis in a set “inverter–induction machine” through multi-dimensional membership function and pattern recognition”, IEEE Transactions Energy Conversion, vol 24, no 2, p 431-441, June 2009 [PAS 02] PASSAGRILLI C., GOBBATO L., TIZIANI R., “Reliability of Au/Al bonding in plastic packages for high temperature (200ºC) and high current applications”, Microelectronics Reliability, vol 42, no 9-11, p 1523-1528, 2002 Faults Detection and Diagnosis in a Static Converter 319 [PRO 67] PROTONOTARIOS E.N., WING O., “Theory of non-uniform RC lines”, IEEE Transactions on Circuit Theory, vol 14, no 1, p 2-12, 1967 [RAM 00] RAMMINGER S., SELIGER N., WACHUTKA G., “Reliability model for AI wire bonds subjected to heel crack failures”, Microelectronics Reliability, vol 40, p 1521-1525, 2000 [REN 01] RENCZ M., SZEKELY V., “Determining partial thermal resistances in a heat flow path with the help of transient measurements”, Proceedings of the 7th THERMINIC Workshop, Paris, p 250-256, 24-27, September 2001 [SAP 90] SAPORTA G., Probabilités, analyse des données et statistique, Technip, Paris, 1990 [SCH 00] SCHULZ-HARDER J et al., “Micro channel water cooled power modules”, PCIM’00, Nuremberg, June 2000 [SCH 03] SCHULZ-HARDER J., “Advantages and new development of direct bonded copper substrates”, Microelectronics Reliability, vol 43, p 359-365, 2003 [SCH 07] SCHULZ-HARDER J., MEYER A., “Hermetic packaging for power multidie modules”, EPE07, 2007 [SZE 88] SZEKELY V., VAN BIEN T., “Fine structure of heat flow path in semiconductor devices: a measurement and identification method”, Solid-State Electronics, vol 31, p 1363-1368, 1988 [THO 95] THORSEN O.V., DALVA M., “A survey of the reliability with an analysis of faults on variable frequency drives in the industry”, European Conference on Power Electronics and Applications, Seville, p 1033-1038, 1995 [ZAD 65] ZADEH L.A., “Fuzzy sets”, Information and Control, vol 8, p 338-353, 1965 [ZID 03] ZIDANI F., DIALLO D., BENBOUZID M.E.H., NAIT SAID M.S., “Induction motor stator faults diagnosis by a current concordia pattern based fuzzy decision system”, IEEE Transactions Energy Conversion, vol 18, no 4, p 469-475, December 2003 [ZID 08] ZIDANI F., DIALLO D., BENBOUZID M.E.H., NAIT SAID R., “A fuzzy-based approach for the diagnosis of fault modes in a voltage-fed PWM inverter induction motor drive”, IEEE Transactions Industrial Electronics, vol 55, no 2, p 586-596, February 2008 List of Authors Imène Ben AMEUR BAZINE LAII Poitiers France and LSE Tunis Tunisia Smail BACHIR LAH Poitiers France Sadok BAZINE LAH Poitiers France and LSE Tunis Tunisia Mohamed BENBOUZID LBMS Brest France 322 Electrical Machines Diagnosis Monia Ben Khader BOUZID LSE Tunis Tunisia Gérard CHAMPENOIS LAII Poitiers France Isabelle CHANTEUR PSA Vélizy-Villacoublay France Guy CLERC Laboratoire AMPERE Lyon France Mikaël CUGNET INES-RDI CEA Grenoble France Claude DELPHA L2S Paris France Demba DIALLO LGEP Paris France Mohamed EL KAMEL OUMAAMAR Electro technical laboratory Constantine Algeria List of Authors Emmanuel FOULON VALEO Systèmes thermiques La Verrière France Sylvie GRUGEON LRCS Amiens France Khaled JELASSI LSE Tunis Tunisia Zoubir KHATIR LTN INRETS Versailles France Stéphane LARUELLE LRCS Amiens France Stéphane LEFEBVRE SATIE CNAM Paris France Luc LORON IREENA Polytech’Nantes France Jean-Claude MARQUES LAGEP Lyon France 323 324 Electrical Machines Diagnosis Sandrine MOREAU LAII Poitiers France Najiba MRABET BELLAAJ LSE Tunis Tunisia Alain OUSTALOUP IMS-LAPS Bordeaux France Thierry POINOT LAII Poitiers France Hubert RAZIK Laboratoire AMPERE Lyon France Jocelyn SABATIER IMS-LAPS Bordeaux France Bernard SAHUT PSA Vélizy-Villacoublay France Emmanuel SCHAEFFER IREENA Polytech’Nantes France List of Authors Jean-Marie TARASCON LRCS Amiens France Jean-Claude TRIGEASSOU IMS-LAPS Bordeaux France 325 Index A activation region, 278 algorithm, 13-15, 18, 23, 24, 26, 45, 46, 58, 60, 62, 63, 66, 71, 72, 75, 78, 85, 90, 94, 95, 104, 108, 118, 119, 126, 138, 163, 173, 196, 197, 232, 236, 237, 255, 261, 278, 280, 283 Marquardt, 173, 236 optimization, 23, 26, 45-47, 60, 75, 137, 173, 180, 181, 184 analysis, 9, 15-17, 47, 49, 50, 56, 71, 75, 93, 125, 127, 137, 163, 169, 176, 183, 193, 195-197, 199, 201, 203, 205, 207, 209, 211, 213, 215, 217, 219-221, 223, 229, 230, 235, 237, 241, 244, 253, 281, 285, 286, 288, 290-292, 313 discriminant, 286, 290-293 linear discriminant Analysis (LDA), 290 multi-dimensional, 74, 285, 286, 290 principal component analysis, 286 spectral, 15, 16, 21, 49, 50, 55-157, 195-197, 200, 203, 205, 211, 214, 216, 235, 237, 238, 241-243 vibrations, 6, 8, 9, 15, 194, 195, 204, 207, 214, 216 angle, 27, 29, 34, 35, 40, 44, 46, 53, 63, 99, 101, 207, 316 electrical and mechanical, 150, 193, 223 γ cc of the stator fault, 34, 46 γ of the rotor fault, 53 a priori information, 23, 60, 61, 63, 71, 85, 88, 90, 93 artificial intelligence, 17, 273 B ball bearing, 8, 193, 205-208, 211, 214, 223 battery state estimator, 168 bar, 2, 3, 5, 7, 10, 15, 30, 32, 51, 52, 55, 56, 62, 63, 71, 93, 99, 129, 194, 195, 199-206, 222, 223, 241, 242, 243, 245, 250, 265, 266, 268 broken, 10, 15, 55, 62, 63, 87-90, 93, 99, 108, 117, 118, 124, 125, 195, 199-206, 227, 228, 232, 233, 241-243, 250, 258, 263-266, 268 cracked, 250, 265 328 Electrical Machines Diagnosis base, 23, 47, 52, 59, 66, 151, 231, 232, 273, 277, 278, 285, 286 learning, 229, 231, 232, 245, 246-249, 253, 255, 257, 259, 263-266, 273, 277, 278, 290, 291, 294 test, 61-65, 72, 75, 78, 82, 87, 119, 121, 131, 141, 149, 153, 154, 156, 158, 160, 161, 163, 180, 184, 199, 208, 231, 232, 243, 246-251, 256-264, 266, 268, 290, 292, 306, 310 brazing, 314, 315 C calculation power, 133, 135-137, 163 classification, 229, 230, 234, 285, 286, 291-294 Concordia contour, 276, 281 transformation, 274, 275 connection wires, 305, 310 constitution of the machines, 153 covariance matrix, 135, 137-139, 142, 157, 174, 287 crankability indicator, 182 D desensitization, 134-136, 162 diagnosis, 1, 3, 11, 12, 15, 18, 25, 45, 47, 55, 57, 59, 66, 71-73, 75, 84, 93-96, 104, 127, 181, 193-223, 227-230, 232, 251, 253, 254, 264, 268, 269, 272-274, 277, 280, 281, 285, 294, 303, 316 in closed loop, 73, 75, 93-94 with model, 23, 24 without model, 66 diffusion phenomenon, 170 diode, 298, 301-303 direct current injection, 159 discretization, 25, 137, 142, 143, 145-147, 157 discrimination, 99, 127, 228, 285, 286, 290-294 E electrical equivalent diagram, 30, 45, 54 error, 14, 15, 23, 45, 58, 59, 63, 71-72, 75, 76, 79, 83, 85, 90, 93, 104, 109, 110, 138, 140-142, 146, 162, 171, 172, 174, 175, 181, 182, 232, 235, 236, 248, 250, 255, 257, 268 equation, 14, 26, 29, 37, 43, 49, 60, 75, 81, 94, 99, 105-107, 109-111, 113, 115, 125, 169, 177, 181, 197, 198, 203, 208, 211, 216, 220, 234, 243, 278, 292 output, 12, 14, 15, 23, 24, 36, 38, 58-60, 71-77, 79, 82, 84, 85, 93, 138-142, 145, 148, 157, 158, 229-233, 235, 236, 245, 247, 251, 254, 257, 263, 265, 266, 268 estimation, 12, 15, 25, 44-46, 51, 58-61, 63, 64, 66, 67, 71-73, 75, 83, 85, 87, 89, 90, 93, 94, 109, 110, 117, 118, 122, 131, 133-139, 141, 142, 146, 148, 154, 157, 159-164, 168, 169, 171, 172, 174, 175, 177, 187, 198, 228, 229, 235-238, 241, 242, 244, 268, 316 parametric, 14, 15, 58, 59, 61, 62, 64, 90, 94, 127, 171, 174, 228, 235, 250, 251, 268, 316 state, 12, 139, 168, experimental results, 27, 131, 133, 157, 309 Index equivalent corrector, 76 electrical parameters, 15, 32, 35, 40, 58, 61, 85, 88, 90, 146, 233, 234, 236, 250, 251, 268 magnetic circuit, 2, 4, 5, 7, 33, 34, 36, 233 rotor squirrel cage, 4, 5, 13, 26, 30, 36, 55, 151 dummy, 29, 32-34, 36, 39-42, 44, 45, 52, 53 winding, 33, 53 F fault, 1-3, 6-10, 13-16, 23-27, 37-40, 42, 44-46, 47, 48, 50-59, 61-63, 66, 71, 84, 87-90, 93-96, 99, 100, 104, 108, 121, 123, 127, 132, 135-137, 157, 193-195, 199, 200, 203, 205-208, 211, 214, 216, 219, 220, 222, 223, 227-258, 261-269, 272-277, 280-282, 284, 285, 289, 291-293, 313, 314 detection, 1, 3, 48, 71, 205, 220 localization, 228, 233, 251 percentage, ring, 3, 5, 7, 10, 16, 30, 32, 55, 129, 151, 152, 154, 156, 163, 199 rotor winding, 27, 33, 34, 52, 53, 66, 67 modeling, 16, 22, 23, 25, 26, 33, 36, 51, 54-56, 66, 72, 74, 75, 93, 96, 108, 110, 117, 133, 142, 146, 157, 169, 170, 171, 175, 235, 273, 316 γ parameter, 53 signature, 58 simultaneous, 55, 58, 63, 73, 84, 87, 90, 123, 136, 148, 232, 234, 329 244, 245, 250, 253, 256, 263, 265, 268 statistics, 194 stator winding, 6, 27, 28, 30, 32-34, 36, 37, 62, 119, 132, 143, 234 Q(pγcc) fault matrix, 42 ηcc parameter, 51 contact resistance, 29, 313, 314 field, 3, 25-27, 30-34, 37-39, 41, 43, 44, 46, 48, 51-53, 55, 66, 77-80, 82, 84, 86, 89, 100-102, 113, 122, 223, 234, 294, 302, 312, 316 rotating, 3-5, 25-30, 34, 35, 37, 43, 44, 66, 77-80, 82, 84, 86, 89, 100, 102, 122, 144, 152 stationary, 17, 26, 27, 38, 39, 41-44, 46, 48, 51-53, 55, 66, 74, 196 fractional behavior, 169 frequency characteristic, 176 fuzzy logic, 17, 18, 273, 274, 280, 281, 285 functions membership, 277, 281, 283, 290, 318 sensitivity, 60, 73, 75, 81-82, 105, 132, 138, 173, 203, 214, 219 H harmonics, 3, 6, 26, 40, 199, 200, 203, 204, 214, 220 I identification, 3, 12-16, 23-25, 34, 58, 59, 62, 63, 66, 71-76, 78-85, 87-90, 93-95, 104, 127, 136, 142, 153, 163, 169, 171, 177, 178, 188, 228, 233, 236, 237, 273 closed loop, 37, 67, 72, 73, 75, 76, 78, 84, 93 330 Electrical Machines Diagnosis IGBT, 151, 294-299, 301-303, 306-308, 314, 316-318 output error, 14, 15, 23, 58, 59, 71-72, 75-76, 79, 85, 93, 105, 106, 110, 111, 115, 171-173, 175, 180, 277, 283, 284 over-parameterized, 78, 83 IGBT chip, 151, 294-299, 301-303, 306-308, 314, 316 inductance, 25, 26, 29, 32-34, 36, 40, 41, 45, 52, 54, 60, 64, 96, 98, 103, 122, 123, 135, 136, 143, 145, 147, 160, 234 leakage, 8, 32, 36, 43, 76, 103, 136, 143, 145, 312 magnetization, 25, 37, 135, 143, 147, 149, 160, 235 instrumentation, 19, 152, 153, 316 insulating substrate, 295, 308 integrated cooler, 300 inter-turn short circuit, 7, 63, 199, 220, 223 inverter arm, 275, 302, 303 J Jacobian, 112, 113, 116, 118, 140 K Kalman filter, 12-15, 67, 75, 94, 111, 121, 131, 134-138, 140-143, 145, 153, 154, 157, 159, 161, 163 L lead-acid battery, 169, 170-172, 175 line amplitude, 207, 211, 214, 219, 222, 237 losses, 1, 3, 7, 8, 25, 45, 51, 133, 134, 143, 144, 146, 147, 302, 315 iron, 5, 25, 45, 51, 134, 143, 144, 146, 147, 191 Joule, 44, 55, 66, 134, 146 M machine, 1, 3-11, 13-16, 18, 24-26, 28-40, 42, 44, 45-50, 52, 53, 55, 57-63, 66, 67, 71-80, 82-85, 87-90, 93, 94, 96, 100, 101, 103, 108, 119, 124, 125, 127, 131-138, 140-146, 149157, 160-163, 193-195, 199, 200, 203, 208, 214-223, 227238, 241-244, 250, 251, 259, 261-265, 267-269, 274, 276, 280, 281, 283, 286, 288 induction, 1, 3-5, 7, 8, 10, 11, 13, 15, 17, 18, 24-26, 30, 31, 37, 43, 45, 50, 53, 55, 57-60, 62, 66, 71-76, 78-80, 82-85, 87-89, 93, 94, 96, 101, 103, 108, 119, 125, 127, 131, 132, 134, 137, 141, 142, 145, 146, 149, 150, 151, 152, 154, 162, 163, 193195, 197, 199-201, 203, 205, 207-209, 211, 213, 214-217, 219-223, 227-230, 232, 233, 244, 259, 267, 268, 269, 274, 281, 283, 284 ring, 151, 152, 154, 156 magnetic saturation, 162, 234 method, 14, 16, 17, 26, 50, 58-61, 71, 74, 76, 78, 93, 95, 106, 109, 123, 125-127, 153, 154, 169, 174, 178, 183-187, 198, 228, 231, 235, 236, 263, 266, 268, 277, 281, 285, 286, 290, 291 Bartlett, 198 Welch, 198 methodology, 61, 67, 73, 82, 84, 196, 316 modeling, 93 monitoring, 1, 10, 11, 13-16, 18, 23, 24, 57, 58, 60, 61, 65, 66, 94, 99, 131-133, 162, 163, 168, 188, 193, 196, 219, 227-230, Index 232-234, 245, 251, 256, 263, 267-269, 274, 275 simplifying assumptions, 25 model, 9, 12-16, 19, 23-26, 38, 40, 44-48, 51, 55, 57-59, 61, 63, 66, 76, 78, 80, 82, 84, 87, 89, 93-95, 102-111, 113, 116, 118, 119, 121, 122, 124-127, 132-143, 145-148, 151-153, 157, 161-163, 169-171, 173181, 183-188, 190-192, 230, 235-237, 273, 280, 299 electrical, 1-6, 8-12, 15, 17, 18-21, 23, 24, 26, 27, 29, 30, 32, 34, 35, 40, 42, 44, 45, 48, 52, 54, 57, 58, 61, 71-72, 75-76, 79-80, 84-85, 88, 90, 93, 95, 96, 101, 115, 131, 133-136, 142-144, 146, 149, 150, 153, 163, 167, 170, 188, 193, 194, 196, 198, 200, 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222, 223, 227, 271, 272, 274, 275, 280, 281, 285, 286, 294, 296, 298, 299, 302, 312, 316 fractional, 60, 75, 80-81, 105, 116, 148, 169, 170, 171, 175-177, 179, 182, 188, 232-234, 254, 266, 277, 278, 283, 291, 292, 314 integer, 154, 169, 174-176, 190 invalidation, 169, 181 linearized, 13, 116, 117, 138, 146 Park, 9, 13, 25, 44, 46, 80, 86, 145, 235-238, 241, 244, 245, 265 Randles, 170, 171 reduced order, 136, 143, 146, 163 thermal, 7, 131-136, 140, 142, 149, 151-153, 157, 162, 163, 171, 188, 272, 295, 296, 298, 331 299, 302, 304-310, 313, 314-316 three-phase, 28-30, 32, 34, 49, 62, 96, 124, 143, 150, 151, 253, 261, 274, 275, 288, 302, 303, 311 validation, 131, 178-180, 183-186 modulus, 295-299 Monte-Carlo simulation, 81 MOSFET, 301 N noise, 8, 18, 58, 61, 63, 73-75, 80, 82-86, 106, 110, 111, 115-118, 123, 135, 139, 141, 159, 171, 174, 178, 183-186, 194, 198, 199, 203, 205-208, 210, 211, 214, 216, 218, 219, 233, 235, 258, 268 correlated, 58, 74, 75, 82-84, 86, 135, 138, 142, 168, 292, 311, 316 white, 82-84, 86, 138, 142, 149, 151, 171, 174, 185, 235 O observer, 13, 15, 94-96, 104, 106, 108-115, 117-127, 141 extended, 1, 13-15, 26, 58, 94-96, 107, 111, 113, 116-119, 122, 124, 125, 127, 131, 134-136, 138-142, 145, 148, 161, 163 high gain, 13, 15, 113, 114, 118, 121 Luenberger, 13, 21, 95, 109 P Park reference frame, 9, 13, 25, 44, 46, 145, 235 periodogram, 197, 198 phase displacement, 262 332 Electrical Machines Diagnosis power electronics, 3, 6, 93, 128, 167, 270, 271, 273, 294, 302, 316 power spectral density (PSD), 50, 157, 197, 237-245, 265, 266 physical interpretation, 28 Concordia reference frame, 277, 280, 282, 284 dummy equivalent winding, 34, 52 rotating vector, 28, 43 Q(pγcc) fault matrix, 42 R real time, 14, 15, 18, 133, 137, 138, 143, 145, 146, 152, 153, 162, 197, 294 reference frame, 25, 28, 29, 34, 35, 41, 42, 44, 45, 48, 49, 53-56, 77-80, 84, 89, 100-103, 115, 122, 124, 146, 274, 276, 277, 280, 282, 284, 301 notation, 26, 27, 35, 44, 56, 66, 111 rotating field, 25-27, 37, 43, 44, 66, 77-80, 82, 84, 86, 89 residual generation, 13, 20, 93 resistance, 5, 13, 15, 21, 25, 32, 34, 36-39, 52, 54, 64, 103, 104, 118, 119, 121-124, 131, 133, 134, 136, 142, 143, 147, 149154, 156, 157, 159-161, 163, 168, 169, 176, 177, 181-189, 234, 250, 251, 283, 299, 303, 305, 306, 308-316 battery, 168-171, 174-178, 181-189 on-state, 302, 303, 305, 310-312, 316 measurements, 150, 151, 156, 159 rotor, 2-10, 13, 15, 16, 25-27, 30-37, 44, 45, 47-49, 51-59, 62, 63, 66, 71, 73, 76, 78-79, 82, 84, 87, 89-93, 97, 99, 100, 101, 103, 104, 115, 118, 119, 121-127, 131-135, 137, 141-146, 148, 149, 151-154, 156-163, 194, 195, 199-206, 216, 220, 222, 223, 227, 228, 230, 232-234, 236, 241-245, 249-251, 253, 256-258, 260, 261, 263-269 S scanning electron microscopy, 306 sequence, 47, 62, 72, 74, 113, 141, 152, 154-156, 160, 174, 196, 203, 245, 246, 249, 250, 255, 265, 266, 268 learning, 245, 246, 253, 255, 265, 266 pseudo binary random, 62, 70 shape recognition, 227, 273 vector, 93 signal processing, 12, 21, 95, 196, 198, 222 sliding, 16, 196, 197, 203 space, 23, 26, 35, 44, 57, 60, 61, 84, 94, 95, 102, 103, 105, 106, 108, 110, 111, 115-118, 122, 124, 126, 127, 145, 147, 151, 152, 278-282, 286-288, 291, 293 detection, 1, 3, 12, 18, 24, 48, 71, 93, 108, 127, 132, 137, 196, 205, 220, 227, 228, 232-235, 237, 244, 246, 247, 249, 250, 268, 269, 272, 277, 279, 281285, 313, 314, 316 localization, 94, 157, 228, 233, 251, 253, 254, 263, 269, 275, 279-281, 283, 284 spectral content, 195-197, 200, 203, 205, 211, 214, 216 spectrum, 16, 25, 48, 93, 171, 194, 195-206, 208-223, 228, 237, 238, 241, 242, 265 residual, 13, 59, 93, 132 Index stator current, 10, 30, 145, 147, 153, 159, 195, 199-203, 205, 209, 211, 212, 214, 215, 220222, 228, 230, 282 vibrations, 204 squirrel cage, 3-5, 13, 15, 26, 30, 36, 55, 151, 152, 163, 165, 194, 200, 224, 259 state, 12, 13, 15, 18, 25, 35, 36, 43, 44, 49, 54, 57, 58, 60, 63, 80, 84, 94, 95, 98, 99, 102-106, 108, 110, 111, 115-118, 122, 124, 126, 127, 134-143, 145-148, 153, 157, 158, 161, 167-169, 171, 188, 199, 219, 222, 228-230, 232, 245, 248, 254, 265-269, 274, 275, 280, 281, 294, 302, 303, 305, 310-312, 316 neural networks, 17, 18, 227, 228, 233, 270, 273, 274, 277, 318 model, 25, 43, 134-136, 138, 139, 145, 146 Park model, 9, 13, 25, 44, 46, 145 representation, 13, 14, 26, 29, 33-35, 39, 40, 44, 45, 57, 60, 84, 94, 100, 105, 179, 229, 272, 273, 275, 280, 282, 285, 286, 288-290, 293 stator winding fault, 37 rotor, 30, 143 symbol, 34, 39, 42, 52 vector, 27-29, 32, 34, 36, 39, 42-44, 46, 58-61, 71, 73, 75-76, 80, 84-85, 93-95, 98, 103, 105, 110, 113-118, 124, 126, 133, 136, 139, 141, 145, 150, 151, 155, 160, 161, 171, 173, 194, 222, 231, 236, 244, 245, 253, 265, 266, 273-277, 280, 282, 284, 285, 287, 292 static converter, 149 eccentricity, 8, 55, 211, 214-219 333 statistical study, stator, 2-10, 13, 15, 24, 25, 27-42, 44, 46-49, 51, 55-58, 62-64, 66, 71, 73, 76, 78-79, 84, 87, 90, 93, 96, 99, 100, 101, 103, 104, 115, 119, 124-127, 131-137, 142-149, 151-154, 156-160, 162, 163, 194, 195, 199, 200-203, 205, 209, 211, 212, 214, 215, 220-223, 227, 228, 230, 232-234, 236, 238, 243-245, 248-251, 253, 254, 256-258, 260-265, 267-269, 274, 282 system, 3, 11-16, 18, 34, 36, 49, 67, 72-77, 79, 81, 93-95, 102, 104-110, 115-117, 124, 132, 136-150, 152, 153, 167, 168, 178-180, 188, 189, 227-229, 232, 233, 245, 251, 256, 267-269, 272-276, 280, 294, 313, 316 experimental, 14, 27, 37-39, 48, 61, 131, 133, 137, 157, 179, 180, 199, 214, 228, 232, 259, 261-264, 273, 309, 316 monitoring, 227, 228, 232, 233, 245, 251, 256, 267, 268, 269 raised, 71, 72, 134, 137, 142, 163, 288, 311 T technique, 13, 16-18, 60, 62, 76, 78, 93, 96, 133, 137, 163, 196, 228, 232, 235, 236, 274, 280, 313 fuzzy-neural, 18 identification, 3, 13, 14, 74, 77, 80, 228 temperature coefficients, 149 cycles, 304, 309 334 Electrical Machines Diagnosis test bench, 119, 122, 124, 125, 131, 149, 150, 163, 199, 231, 259, 266 thermal cycling, 307, 309, 315 fatigue, 7, 296, 304, 313 thermocouples, 151, 152, 156 three-phase inverter, 150, 151 transform, 17, 28, 29, 31-33, 45, 80, 101, 172, 180, 196, 197, 228, 286, 288 discrete Fourier transform (DTF), 196, 197 Fourier, 19, 172, 180, 196, 197, 228 transistor, 294, 297, 298, 311 V vector control, 46, 61, 71, 75-76, 126, 133, 136, 150, 151, 155, 160, 161, 222 W Warburg cell, 170 weighting window, 198 .. .Electrical Machines Diagnosis Electrical Machines Diagnosis Edited by Jean- Claude Trigeassou First published 2011 in Great Britain and the United States by ISTE Ltd and John... ? ?Diagnosis of induction machines by parameter estimation”, in Control Methods for Electrical Machines, edited by René Husson, ISTE Ltd., and John Wiley, 2009 xiv Electrical Machines Diagnosis In Chapter... them Jean- Claude TRIGEASSOU July 2011 Chapter Faults in Electrical Machines and their Diagnosis 1.1 Introduction This chapter gives an overview of faults found in electrical machines and their diagnosis,