Abdenour Soualhi - Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives

Elias G. Strangas

Michigan State UniversityEast Lansing, Michigan

Guy Clerc

University of LyonVilleurbanne, France

Hubert Razik

University of LyonVilleurbanne, France

Abdenour Soualhi

University of LyonSaint Etienne, France

This edition first published 2022

© 2022 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

Edition History

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From the authors to their families

1 Cover

2 Title page Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives Elias G. Strangas Michigan State UniversityEast Lansing, Michigan Guy Clerc University of LyonVilleurbanne, France Hubert Razik University of LyonVilleurbanne, France Abdenour Soualhi University of LyonSaint Etienne, France

3 Copyright

4 Dedication

5 Contributors

6 Acknowledgments

7 Acronyms

8 Introduction

9 1 Basic Methods and Tools 1.1 General Approach1.2 Feature Extraction: Signal and Preconditioning1.2.1 Raw Signals: What Kind of Signals and Sensors?1.2.1.1 Current Sensors1.2.1.2 Vibration Measurement and Accelerometers1.2.1.3 Temperature Sensors1.2.1.4 Field Sensors1.2.1.5 Acoustic Sensors1.2.1.6 Other Sensors1.2.2 Preconditioning1.2.2.1 Signal Features in the Time Domain1.2.2.2 Symmetric Component, Park Component1.2.2.3 Symmetric Component, Park Component1.2.2.4 Signal Features in the Frequency Domain1.2.2.5 Wavelet Analysis1.2.2.6 Instantaneous Amplitude and Frequency1.2.2.7 Bilinear Time–frequency Distributions or Quadratic Time–frequency Distributions: Cohen’s Class1.2.2.7.a Uncertainty Principle of Heisenberg1.2.2.7.b General Representation1.2.2.7.c Properties1.2.2.7.d Different Representations1.2.2.8 Statistic Features1.2.2.9 Cyclostationarity1.2.3 Model Approach1.2.3.1 Kalman Observer1.2.3.2 Extended Observer1.2.3.3 Unscented Kalman Filter1.2.4 Parity Space1.3 Feature Reduction, Principal Component Analysis1.3.1 Principal Component Analysis: A Space Reduction and an Unsupervised Classification1.3.2 Intercorrelation1.3.2.1 Pearson Coefficient “r”1.3.2.2 Spearman Coefficient “ b”1.3.3 Information Content: Shannon Entropy1.3.4 Pattern Sizing Reduction for a Supervised Classification1.3.4.1 Selection Criteria1.3.4.2 Sequential Backward Feature Selection and Sequential Forward Feature Selection1.3.5 Pattern Sizing Reduction for an Unsupervised Classification: Laplacian Score1.3.6 Choice of the Number of Classes for an Unsupervised Classification1.3.6.1 Choice of the Number of Classes with a PCA1.3.6.2 General Case1.3.7 Other Quality Criteria of a Classification1.3.7.1 R 2index1.3.7.2 Calinski–Harabasz Index1.3.7.3 Davies–Bouldin Index1.3.7.4 Silhouette Index1.3.7.5 Dunn Index1.4 Classification Methods1.4.1 Generalities1.4.1.1 Supervised and Unsupervised Clustering1.4.1.2 Measuring the Similarity: Different Distances1.4.2 Supervised Clustering1.4.2.1 k Nearest Neighbors1.4.2.2 Support Vector Machine1.4.2.3 Recurrent Neural Network1.4.3 Unsupervised Clustering1.4.3.1 Hierarchical Classification1.4.3.2 K-means and Centroid Clustering1.4.3.3 Self-organizing Map1.5 Prognosis Methods1.5.1 Prognosis Process1.5.2 Time Series Extrapolation Methods1.5.3 Bayesian Inference1.5.4 Markov Chain1.5.5 Hidden Markov Models1.5.6 Rainflow1.5.6.1 Hidden Semi-Markov ModelsReferences

10 2 Applications and Specifics 2.1 General Presentation of Motor Drives2.2 Electrical Machines2.2.1 Basics2.2.2 Magnetic Steel and Magnets2.2.3 Windings and Insulation2.3 Machine Models, Operation, and Control2.3.1 Three-phase Windings2.3.2 Induction Machines2.3.2.1 Induction Machine Rotor Field Orientation2.3.2.2 Direct Torque Control2.3.3 Permanent Magnet AC Machines2.4 Faults in Electrical Machines2.4.1 Operational Variables and Measurements2.4.2 Supervision, Detection, and Fault Classification2.4.3 Bearings2.4.4 Insulation2.5 Open and Short Faults, Eccentricity, Broken Magnets and Rotor Bars2.5.1 Induction Machines2.5.1.1 Stator Fault Diagnosis2.5.1.2 Eccentricity2.5.1.3 Multi-fault Diagnosis with Stray Flux and Flux Sensor2.5.1.4 Open Faults in Windings and Inverter2.5.1.5 Broken Rotor Bars2.5.2 Permanent Magnet AC Machines2.5.2.1 Demagnetization of Permanent Magnets2.5.2.2 Open and Short Circuit2.5.3 Sensor Faults2.5.4 Fault Mitigation and Management2.6 Power Electronics and Systems2.6.1 A Brief Description of Power Electronics in AC drives2.6.2 A Brief Description of Static Switches2.6.2.1 MOSFET2.6.2.2 IGBT2.6.2.3 Si and SiC Technology2.6.2.4 Thermal Behavior2.6.3 A Brief Description of Capacitors2.6.3.1 General Description2.6.3.2 Different Kinds of Capacitors2.6.3.2.a Non-polarized Capacitors2.6.3.2.b Polarized Capacitors2.6.4 Device Faults and Their Manifestation2.6.4.1 Basic Notion2.6.4.2 On Chip Failures2.6.4.3 Packaging and Chip Environment Failures2.6.5 Capacitor Failure Modes2.6.5.1 Failure by Degradation2.6.5.2 Catastrophic Failure2.6.6 Diagnosis and Prognosis Techniques for Power Devices2.6.6.1 Introduction2.6.6.2 Failure Modes Indicators and TSEP for Power Electronic Devices2.6.6.3 Diagnosis of Failure Modes2.6.6.3.a Diagnosis based on the Direct Analysis of the Current2.6.6.3.b Diagnosis based on the Direct or Indirect Analysis of Junction Temperature2.6.6.3.c Diagnosis based on Signal Processing2.6.6.3.d Diagnosis based on Clustering2.6.6.3.e Diagnosis based on Neural Network2.6.6.3.f Synthesis2.6.6.4 Prognosis of Failure Modes2.6.6.4.a Prognosis based on Failure Mechanism and Statistical Data2.6.6.4.b Prognosis based on Failure Precursors2.6.7 Diagnosis and Prognosis Techniques for Capacitors2.6.7.1 Fault Diagnosis Techniques2.6.7.2 Methods for Predicting Electrolytic Capacitor FailuresBibliography