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E-grāmata: Electrical Machines Diagnosis

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  • Formāts: EPUB+DRM
  • Izdošanas datums: 07-Feb-2013
  • Izdevniecība: ISTE Ltd and John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118601754
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Electrical Machines DiagnosisPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 07-Feb-2013
  • Izdevniecība: ISTE Ltd and John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118601754
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Concentrating primarily on results from French researchers, this book addresses monitoring and diagnosis of electrical machine faults and compiles techniques used to detect the electrical, thermal, and mechanical faults that occur in electrical drives. Topics include: modeling induction machine winding faults for diagnosis, closed-loop diagnosis of the induction machine, diagnosis using observers, thermal monitoring, resistance and crankability estimation, signal analysis, fault diagnosis of the induction machine by neural networks, and faults detection and diagnosis in static converters. While extremely technical, writing is concise and direct and well-supported by numerous illustrations. Editor Trigeassou (IMS-LAPS, Bordeaux U., France) and 29 co-authors contributed. Annotation ©2011 Book News, Inc., Portland, OR (booknews.com)

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.
Preface xi
Chapter 1 Faults in Electrical Machines and their Diagnosis
1(22)
Sadok Bazine
Jean-Claude Trigeassou
1.1 Introduction
1(2)
1.2 Composition of induction machines
3(2)
1.2.1 The stator
4(1)
1.2.2 The rotor
4(1)
1.2.3 Bearings
5(1)
1.3 Failures in induction machines
5(5)
1.3.1 Mechanical failures
8(1)
1.3.2 Electrical failures
9(1)
1.4 Overview of methods for diagnosing induction machines
10(8)
1.4.1 Diagnosis methods using an analytical model
12(4)
1.4.2 Diagnostic methods with no analytical model
16(2)
1.5 Conclusion
18(1)
1.6 Bibliography
19(4)
Chapter 2 Modeling Induction Machine Winding Faults for Diagnosis
23(46)
Emmanuel Schaeffer
Smail Bachir
2.1 Introduction
23(3)
2.1.1 Simulation model versus diagnosis model
23(1)
2.1.2 Objectives
24(1)
2.1.3 Methodology
24(1)
2.1.4
Chapter structure
25(1)
2.2 Study framework and general methodology
26(14)
2.2.1 Working hypotheses
26(1)
2.2.2 Equivalence between winding systems
27(7)
2.2.3 Equivalent two-phase machine with no fault
34(3)
2.2.4 Consideration of a stator winding fault
37(3)
2.3 Model of the machine with a stator insulation fault
40(11)
2.3.1 Electrical equations of the machine with a stator short-circuit
40(3)
2.3.2 State model in any reference frame
43(4)
2.3.3 Extension of the three-phase stator model
47(1)
2.3.4 Model validation
48(3)
2.4 Generalization of the approach to the coupled modeling of stator and rotor faults
51(6)
2.4.1 Electrical equations in the presence of rotor imbalance
53(2)
2.4.2 Generalized model of the machine with stator and rotor faults
55(2)
2.5 Methodology for monitoring the induction machine
57(7)
2.5.1 Parameter estimation for induction machine diagnosis
58(3)
2.5.2 Experimental validation of the monitoring strategy
61(3)
2.6 Conclusion
64(3)
2.7 Bibliography
67(2)
Chapter 3 Closed-Loop Diagnosis of the Induction Machine
69(24)
Imene Ben Ameur Bazine
Jean-Claude Trigeassou
Khaled Jelassi
Thierry Poinot
3.1 Introduction
69(2)
3.2 Closed-loop identification
71(3)
3.2.1 Problems in closed-loop identification
71(2)
3.2.2 Identification problems for diagnosing electrical machines
73(1)
3.3 General methodology of closed-loop identification of induction machine
74(8)
3.3.1 Taking control into account
74(2)
3.3.2 Machine identification by closed-loop decomposition
76(4)
3.3.3 Identification results
80(2)
3.4 Closed-loop diagnosis of simultaneous stator/rotor faults
82(7)
3.4.1 General model of induction machine faults
82(1)
3.4.2 Parameter estimation with a priori information
83(1)
3.4.3 Detection and localization
84(3)
3.4.4 Comparison of identification results through direct and indirect approaches
87(2)
3.5 Conclusion
89(1)
3.6 Bibliography
90(3)
Chapter 4 Induction Machine Diagnosis Using Observers
93(38)
Guy Clerc
Jean-Claude Marques
4.1 Introduction
93(3)
4.2 Model presentation
96(8)
4.2.1 Three-phase model of induction machine without fault
96(4)
4.2.2 Park's model of an induction machine without fault
100(4)
4.2.3 Induction machine models with fault
104(1)
4.3 Observers
104(15)
4.3.1 Principle
104(4)
4.3.2 Different kinds of observers
108(7)
4.3.3 Extended observer
115(4)
4.4 Applying observers to diagnostics
119(8)
4.4.1 Using Park's model
119(5)
4.4.2 Use of the three-phase model
124(1)
4.4.3 Spectral analysis of the torque reconstructed by the observer
125(2)
4.5 Conclusion
127(1)
4.6 Bibliography
128(3)
Chapter 5 Thermal Monitoring of the Induction Machine
131(36)
Luc Loron
Emmanuel Foulon
5.1 Introduction
131(6)
5.1.1 Aims of the thermal monitoring on induction machines
131(2)
5.1.2 Main methods of thermal monitoring of the induction machines
133(4)
5.2 Real-time parametric estimation by Kalman filter
137(5)
5.2.1 Interest and specificities of the Kalman filter
137(1)
5.2.2 Implementation of an extended Kalman filter
138(4)
5.3 Electrical models for the thermal monitoring
142(7)
5.3.1 Continuous time models
143(1)
5.3.2 Full-order model
144(3)
5.3.3 Discretized and extended model
147(2)
5.4 Experimental system
149(8)
5.4.1 General presentation of the test bench
149(2)
5.4.2 Thermal instrumentation
151(2)
5.4.3 Electrical instrumentation
153(4)
5.5 Experimental results
157(5)
5.5.1 Tuning of the Kalman filter
157(3)
5.5.2 Influence of the magnetic saturation
160(2)
5.6 Conclusion
162(1)
5.7 Appendix: induction machine characteristics
163(1)
5.8 Bibliography
163(4)
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(26)
Jocelyn Sabatier
Mikael Cugnet
Stephane Laruelle
Sylvie Grugeon
Isabelle Chanteur
Bernard Sahut
Alain Oustaloup
Jean-Marie Tarascon
6.1 Introduction
167(2)
6.2 Fractional model of a lead-acid battery for the start-up phase
169(2)
6.3 Identification of the fractional model
171(4)
6.3.1 Output error identification algorithm
171(2)
6.3.2 Calculation of the output sensitivities
173(1)
6.3.3 Validation of the estimated parameters
174(1)
6.3.4 Application to start-up signals
174(1)
6.4 Battery resistance as crankability estimator
175(3)
6.5 Model validation and estimation of the battery resistance
178(10)
6.5.1 Frequency approach of the model validation
178(3)
6.5.2 Application to the estimation of the battery resistance
181(3)
6.5.3 Simplified resistance estimator
184(4)
6.6 Toward a battery state estimator
188(1)
6.7 Conclusion
188(2)
6.8 Bibliography
190(3)
Chapter 7 Electrical and Mechanical Faults Diagnosis of Induction Machines using Signal Analysis
193(34)
Hubert Razik
Mohamed El Kamel Oumaamar
7.1 Introduction
193(1)
7.2 The spectrum of the current line
194(2)
7.3 Signal processing
196(3)
7.3.1 Fourier's transform
196(1)
7.3.2 Periodogram
197(2)
7.4 Signal analysis from experiment campaigns
199(23)
7.4.1 Disturbances induced by a broken bar
199(6)
7.4.2 Bearing faults
205(6)
7.4.3 Static eccentricity
211(9)
7.4.4 Inter turn short circuits
220(2)
7.5 Conclusion
222(1)
7.6 Appendices
223(1)
7.6.1 Appendix A
223(1)
7.6.2 Appendix B
223(1)
7.7 Bibliography
224(3)
Chapter 8 Fault Diagnosis of the Induction Machine by Neural Networks
227(44)
Monia Ben Khader Bouzid
Najiba Mrabet Bellaaj
Khaled Jelassi
Gerard Champenois
Sandrine Moreau
8.1 Introduction
227(1)
8.2 Methodology of the use of the ANN in the diagnostic domain
228(4)
8.2.1 Choice of the fault indicators
229(1)
8.2.2 Choice of the structure of the network
230(1)
8.2.3 Construction of the learning and test base
231(1)
8.2.4 Learning and test of the network
232(1)
8.3 Description of the monitoring system
232(1)
8.4 The detection problem
233(2)
8.5 The proposed method for the robust detection
235(2)
8.5.1 Generation of the estimated residues
236(1)
8.6 Signature of the stator and rotor faults
237(7)
8.6.1 Analysis of the residue in healthy regime
237(1)
8.6.2 Analysis of the residue in presence of the stator fault
237(4)
8.6.3 Analysis of the residue in presence of the rotor fault
241(3)
8.6.4 Analysis of the residue in presence of simultaneous stator/rotor fault
244(1)
8.7 Detection of the faults by the RNd neural network
244(7)
8.7.1 Extraction of the fault indicators
244(1)
8.7.2 Learning sequence of the RNd network
245(1)
8.7.3 Structure of the RNd network
246(1)
8.7.4 Results of the learning of the RNd network
247(1)
8.7.5 Test results of the RNd network
248(3)
8.8 Diagnosis of the stator fault
251(12)
8.8.1 Choice of the fault indicators for the RNcc network
251(2)
8.8.2 Learning sequence of the RNcc network
253(1)
8.8.3 Structure of the RNcc network
254(1)
8.8.4 Learning results of the RNcc network
255(1)
8.8.5 Results of the test of the RNcc network
256(3)
8.8.6 Experimental validation of the RNcc network
259(4)
8.9 Diagnosis of the rotor fault
263(4)
8.9.1 Choice of the fault indicators of the RNbc network
265(1)
8.9.2 Learning sequence of the RNbc network
265(1)
8.9.3 Learning, test and validation results
266(1)
8.10 Complete monitoring system of the induction machine
267(1)
8.11 Conclusion
268(1)
8.12 Bibliography
269(2)
Chapter 9 Faults Detection and Diagnosis in a Static Converter
271(50)
Mohamed Benbouzid
Claude Delpha
Zoubir Khatir
Stephane Lefebvre
Demba Diallo
9.1 Introduction
271(2)
9.2 Detection and diagnosis
273(21)
9.2.1 Neural network approach
273(7)
9.2.2 A fuzzy logic approach
280(5)
9.2.3 Multi-dimensional data analysis
285(9)
9.3 Thermal fatigue of power electronic moduli and failure modes
294(22)
9.3.1 Presentation of power electronic moduli in diagnosis
294(10)
9.3.2 Causes and main types of degradation of power electronics moduli
304(6)
9.3.3 Interconnection degradation effects on electrical characteristics and potential use for diagnosis
310(3)
9.3.4 Effects of interface degradation on thermal characteristics and potential use for diagnosis
313(3)
9.4 Conclusion
316(1)
9.5 Bibliography
316(5)
List of Authors 321(6)
Index 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.
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