Contributors xiiiAcknowledgments xvAcronyms xviiIntroduction xxi1 Basic Methods and Tools 11.1 General Approach 11.2 Feature Extraction: Signal and Preconditioning 21.2.1 Raw Signals: What Kind of Signals and Sensors? 21.2.1.1 Current Sensors 31.2.1.2 Vibration Measurement and Accelerometers 131.2.1.3 Temperature Sensors 141.2.1.4 Field Sensors 161.2.1.5 Acoustic Sensors 161.2.1.6 Other Sensors 181.2.2 Preconditioning 221.2.2.1 Signal Features in the Time Domain 221.2.2.2 Symmetric Component, Park Component 221.2.2.3 Symmetric Component, Park Component 241.2.2.4 Signal Features in the Frequency Domain 261.2.2.5 Wavelet Analysis 341.2.2.6 Instantaneous Amplitude and Frequency 351.2.2.7 Bilinear Time-frequency Distributions or QuadraticTime-frequency Distributions: Cohen's Class 361.2.2.7.a Uncertainty Principle of Heisenberg 371.2.2.7.b General Representation 371.2.2.7.c Properties 381.2.2.7.d Different Representations 391.2.2.8 Statistic Features 451.2.2.9 Cyclostationarity 461.2.3 Model Approach 481.2.3.1 Kalman Observer 511.2.3.2 Extended Observer 521.2.3.3 Unscented Kalman Filter 551.2.4 Parity Space 561.3 Feature Reduction, Principal Component Analysis 601.3.1 Principal Component Analysis: A Space Reduction and an Unsupervised Classification 601.3.2 Intercorrelation 621.3.2.1 Pearson Coefficient "r" 621.3.2.2 Spearman Coefficient "rho" 631.3.3 Information Content: Shannon Entropy 651.3.4 Pattern Sizing Reduction for a Supervised Classification 651.3.4.1 Selection Criteria 651.3.4.2 Sequential Backward Feature Selection and Sequential Forward Feature Selection 671.3.5 Pattern Sizing Reduction for an Unsupervised Classification: Laplacian Score 681.3.6 Choice of the Number of Classes for an Unsupervised Classification 691.3.6.1 Choice of the Number of Classes with a PCA 691.3.6.2 General Case 701.3.7 Other Quality Criteria of a Classification 711.3.7.1 R²index 711.3.7.2 Calinski-Harabasz Index 721.3.7.3 Davies-Bouldin Index 731.3.7.4 Silhouette Index 731.3.7.5 Dunn Index 741.4 Classification Methods 741.4.1 Generalities 741.4.1.1 Supervised and Unsupervised Clustering 751.4.1.2 Measuring the Similarity: Different Distances 761.4.2 Supervised Clustering 771.4.2.1 k Nearest Neighbors 781.4.2.2 Support Vector Machine 801.4.2.3 Recurrent Neural Network 821.4.3 Unsupervised Clustering 851.4.3.1 Hierarchical Classification 861.4.3.2 K-means and Centroid Clustering 891.4.3.3 Self-organizing Map 901.5 Prognosis Methods 931.5.1 Prognosis Process 931.5.2 Time Series Extrapolation Methods 951.5.3 Bayesian Inference 1011.5.4 Markov Chain 1031.5.5 Hidden Markov Models 1051.5.6 Rainflow 1101.5.6.1 Hidden Semi-Markov Models 114References 1142 Applications and Specifics 1252.1 General Presentation of Motor Drives 1252.2 Electrical Machines 1262.2.1 Basics 1282.2.2 Magnetic Steel and Magnets 1292.2.3 Windings and Insulation 1332.3 Machine Models, Operation, and Control 1372.3.1 Three-phase Windings 1372.3.2 Induction Machines 1382.3.2.1 Induction Machine Rotor Field Orientation 1402.3.2.2 Direct Torque Control 1412.3.3 Permanent Magnet AC Machines 1442.4 Faults in Electrical Machines 1462.4.1 Operational Variables and Measurements 1472.4.2 Supervision, Detection, and Fault Classification 1492.4.3 Bearings 1532.4.4 Insulation 1692.5 Open and Short Faults, Eccentricity, Broken Magnets and Rotor Bars 1802.5.1 Induction Machines 1822.5.1.1 Stator Fault Diagnosis 1832.5.1.2 Eccentricity 1912.5.1.3 Multi-fault Diagnosis with Stray Flux and Flux Sensor 1932.5.1.4 Open Faults in Windings and Inverter 1952.5.1.5 Broken Rotor Bars 1962.5.2 Permanent Magnet AC Machines 1982.5.2.1 Demagnetization of Permanent Magnets 1982.5.2.2 Open and Short Circuit 2012.5.3 Sensor Faults 2082.5.4 Fault Mitigation and Management 2092.6 Power Electronics and Systems 2152.6.1 A Brief Description of Power Electronics in AC drives 2162.6.2 A Brief Description of Static Switches 2232.6.2.1 MOSFET 2232.6.2.2 IGBT 2302.6.2.3 Si and SiC Technology 2362.6.2.4 Thermal Behavior 2362.6.3 A Brief Description of Capacitors 2412.6.3.1 General Description 2412.6.3.2 Different Kinds of Capacitors 2452.6.3.2.a Non-polarized Capacitors 2452.6.3.2.b Polarized Capacitors 2512.6.4 Device Faults and Their Manifestation 2552.6.4.1 Basic Notion 2572.6.4.2 On Chip Failures 2582.6.4.3 Packaging and Chip Environment Failures 2592.6.5 Capacitor Failure Modes 2612.6.5.1 Failure by Degradation 2612.6.5.2 Catastrophic Failure 2622.6.6 Diagnosis and Prognosis Techniques for Power Devices 2622.6.6.1 Introduction 2622.6.6.2 Failure Modes Indicators and TSEP for Power Electronic Devices 2622.6.6.3 Diagnosis of Failure Modes 2692.6.6.3.a Diagnosis based on the Direct Analysis of the Current 2712.6.6.3.b Diagnosis based on the Direct or Indirect Analysis of Junction Temperature 2792.6.6.3.c Diagnosis based on Signal Processing 2842.6.6.3.d Diagnosis based on Clustering 2882.6.6.3.e Diagnosis based on Neural Network 2912.6.6.3.f Synthesis 2952.6.6.4 Prognosis of Failure Modes 2952.6.6.4.a Prognosis based on Failure Mechanism and Statistical Data 2952.6.6.4.b Prognosis based on Failure Precursors 3002.6.7 Diagnosis and Prognosis Techniques for Capacitors 3102.6.7.1 Fault Diagnosis Techniques 3102.6.7.2 Methods for Predicting Electrolytic Capacitor Failures 318Bibliography 3243 Fault Diagnosis and Prognosis for Reliability Enhancement 3453.1 Introduction 3453.2 Fundamentals 3463.2.1 The Pattern of Failures with Time for Non-Repairable Items 3503.2.2 Distribution Functions 3503.2.3 Confidence in Reliability and Prognosis 3533.3 Component Reliability 3543.4 Reliability of Subsystems and Systems 3613.4.1 Analysis Tools 3613.5 Lifetime, Reliability Prediction 3653.6 Fault Management and Mitigation 3683.7 Design and Manufacturing 3713.8 Applications and Case Studies 3723.9 Scheduled Maintenance, Condition-Based Maintenance 3973.9.1 Reliability and Costs 4073.10 Conclusions 409Bibliography 410Index 415
Elias G. Strangas, PhD, is a Professor at Michigan State University, where he heads the Electrical Machines and Drives Laboratory.Guy Clerc is a Professor at the Université de Lyon, Ampère, in Villeurbanne, France.Hubert Razik is a Professor at the Université de Lyon, Ampère, in Villeurbanne, France.Abdenour Soualhi is an Assistant Professor at the LASPI Laboratory in Jean Monnet University Roanne.