Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives (IEEE Press)

✍ Scribed by Elias G. Strangas, Guy Clerc, Hubert Razik, Abdenour Soualhi

• Publisher: Wiley-IEEE Press
• Year: 2021
• Tongue: English
• Leaves: 445
• Edition: 1
• Category: Library

✦ Synopsis

Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives

An insightful treatment of present and emerging technologies in fault diagnosis and failure prognosis

In Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives, a team of distinguished researchers delivers a comprehensive exploration of current and emerging approaches to fault diagnosis and failure prognosis of electrical machines and drives. The authors begin with foundational background, describing the physics of failure, the motor and drive designs and components that affect failure and signals, signal processing, and analysis.

The book then moves on to describe the features of these signals and the methods commonly used to extract these features to diagnose the health of a motor or drive, as well as the methods used to identify the state of health and differentiate between possible faults or their severity.

Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives discusses the tools used to recognize trends towards failure and the estimation of remaining useful life. It addresses the relationships between fault diagnosis, failure prognosis, and fault mitigation.

The book also provides:

• A thorough introduction to the modes of failure, how early failure precursors manifest themselves in signals, and how features extracted from these signals are processed
• A comprehensive exploration of the fault diagnosis, the results of characterization, and how they used to predict the time of failure and the confidence interval associated with it
• A focus on medium-sized drives, including induction, permanent magnet AC, reluctance, and new machine and drive types

Perfect for researchers and students who wish to study or practice in the rea of electrical machines and drives, Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives is also an indispensable resource for researchers with a background in signal processing or statistics.

✦ Table of Contents

Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives
Contents
Contributors
Acknowledgments
Acronyms
Introduction
1 Basic Methods and Tools
1.1 General Approach
1.2 Feature Extraction: Signal and Preconditioning
1.2.1 Raw Signals: What Kind of Signals and Sensors?
1.2.1.1 Current Sensors
1.2.1.2 Vibration Measurement and Accelerometers
1.2.1.3 Temperature Sensors
1.2.1.4 Field Sensors
1.2.1.5 Acoustic Sensors
1.2.1.6 Other Sensors
1.2.2 Preconditioning
1.2.2.1 Signal Features in the Time Domain
1.2.2.2 Symmetric Component, Park Component
1.2.2.3 Symmetric Component, Park Component
1.2.2.4 Signal Features in the Frequency Domain
1.2.2.5 Wavelet Analysis
1.2.2.6 Instantaneous Amplitude and Frequency
1.2.2.7 Bilinear Time–frequency Distributions or Quadratic
Time–frequency Distributions: Cohen’s Class
1.2.2.7.a Uncertainty Principle of Heisenberg
1.2.2.7.b General Representation
1.2.2.7.c Properties
1.2.2.7.d Different Representations
1.2.2.8 Statistic Features
1.2.2.9 Cyclostationarity
1.2.3 Model Approach
1.2.3.1 Kalman Observer
1.2.3.2 Extended Observer
1.2.3.3 Unscented Kalman Filter
1.2.4 Parity Space
1.3 Feature Reduction, Principal Component
Analysis
1.3.1 Principal Component Analysis: A Space Reduction and an Unsupervised Classification
1.3.2 Intercorrelation
1.3.2.1 Pearson Coefficient “r"
1.3.2.2 Spearman Coefficient “𝜌 "
1.3.3 Information Content: Shannon Entropy
1.3.4 Pattern Sizing Reduction for a Supervised Classification
1.3.4.1 Selection Criteria
1.3.4.2 Sequential Backward Feature Selection and Sequential Forward Feature Selection
1.3.5 Pattern Sizing Reduction for an Unsupervised Classification:
Laplacian Score
1.3.6 Choice of the Number of Classes for an Unsupervised Classification
1.3.6.1 Choice of the Number of Classes with a PCA
1.3.6.2 General Case
1.3.7 Other Quality Criteria of a Classification
1.3.7.1 R2 index
1.3.7.2 Calinski–Harabasz Index
1.3.7.3 Davies–Bouldin Index
1.3.7.4 Silhouette Index
1.3.7.5 Dunn Index
1.4 Classification Methods
1.4.1 Generalities
1.4.1.1 Supervised and Unsupervised Clustering
1.4.1.2 Measuring the Similarity: Different Distances
1.4.2 Supervised Clustering
1.4.2.1 k Nearest Neighbors
1.4.2.2 Support Vector Machine
1.4.2.3 Recurrent Neural Network
1.4.3 Unsupervised Clustering
1.4.3.1 Hierarchical Classification
1.4.3.2 K-means and Centroid Clustering
1.4.3.3 Self-organizing Map
1.5 Prognosis Methods
1.5.1 Prognosis Process
1.5.2 Time Series Extrapolation Methods
1.5.3 Bayesian Inference
1.5.4 Markov Chain
1.5.5 Hidden Markov Models
1.5.6 Rainflow
1.5.6.1 Hidden Semi-Markov Models
References
2 Applications and Specifics
2.1 General Presentation of Motor Drives
2.2 Electrical Machines
2.2.1 Basics
2.2.2 Magnetic Steel and Magnets
2.2.3 Windings and Insulation
2.3 Machine Models, Operation, and Control
2.3.1 Three-phase Windings
2.3.2 Induction Machines
2.3.2.1 Induction Machine Rotor Field Orientation
2.3.2.2 Direct Torque Control
2.3.3 Permanent Magnet AC Machines
2.4 Faults in Electrical Machines
2.4.1 Operational Variables and Measurements
2.4.2 Supervision, Detection, and Fault Classification
2.4.3 Bearings
2.4.4 Insulation
2.5 Open and Short Faults, Eccentricity, Broken Magnets and Rotor Bars
2.5.1 Induction Machines
2.5.1.1 Stator Fault Diagnosis
2.5.1.2 Eccentricity
2.5.1.3 Multi-fault Diagnosis with Stray Flux and Flux Sensor
2.5.1.4 Open Faults in Windings and Inverter
2.5.1.5 Broken Rotor Bars
2.5.2 Permanent Magnet AC Machines
2.5.2.1 Demagnetization of Permanent Magnets
2.5.2.2 Open and Short Circuit
2.5.3 Sensor Faults
2.5.4 Fault Mitigation and Management
2.6 Power Electronics and Systems
2.6.1 A Brief Description of Power Electronics in AC drives
2.6.2 A Brief Description of Static Switches
2.6.2.1 MOSFET
2.6.2.2 IGBT
2.6.2.3 Si and SiC Technology
2.6.2.4 Thermal Behavior
2.6.3 A Brief Description of Capacitors
2.6.3.1 General Description
2.6.3.2 Different Kinds of Capacitors
2.6.3.2.a Non-polarized Capacitors
2.6.3.2.b Polarized Capacitors
2.6.4 Device Faults and Their Manifestation
2.6.4.1 Basic Notion
2.6.4.2 On Chip Failures
2.6.4.3 Packaging and Chip Environment Failures
2.6.5 Capacitor Failure Modes
2.6.5.1 Failure by Degradation
2.6.5.2 Catastrophic Failure
2.6.6 Diagnosis and Prognosis Techniques for Power Devices
2.6.6.1 Introduction
2.6.6.2 Failure Modes Indicators and TSEP for Power Electronic Devices
2.6.6.3 Diagnosis of Failure Modes
2.6.6.3.a Diagnosis based on the Direct Analysis of the Current
2.6.6.3.b Diagnosis based on the Direct or Indirect Analysis of Junction Temperature
2.6.6.3.c Diagnosis based on Signal Processing
2.6.6.3.d Diagnosis based on Clustering
2.6.6.3.e Diagnosis based on Neural Network
2.6.6.3.f Synthesis
2.6.6.4 Prognosis of Failure Modes
2.6.6.4.a Prognosis based on Failure Mechanism and Statistical Data
2.6.6.4.b Prognosis based on Failure Precursors
2.6.7 Diagnosis and Prognosis Techniques for Capacitors
2.6.7.1 Fault Diagnosis Techniques
2.6.7.2 Methods for Predicting Electrolytic Capacitor Failures
Bibliography
3 Fault Diagnosis and Prognosis for Reliability Enhancement
3.1 Introduction
3.2 Fundamentals
3.2.1 The Pattern of Failures with Time for Non-Repairable Items
3.2.2 Distribution Functions
3.2.3 Confidence in Reliability and Prognosis
3.3 Component Reliability
3.4 Reliability of Subsystems and Systems
3.4.1 Analysis Tools
3.5 Lifetime, Reliability Prediction
3.6 Fault Management and Mitigation
3.7 Design and Manufacturing
3.8 Applications and Case Studies
3.9 Scheduled Maintenance, Condition-Based Maintenance
3.9.1 Reliability and Costs
3.10 Conclusions
Bibliography
Index
EULA