Prognostics and health management of electronics. (2018)

• Record Type: Book
• Further Information: Note: By Michael G. Pecht and Myeongsu Kang.
• Authors: Pecht, Michael; Kang, Myeongsu, 1980-
• Contents: List of Contributors xxiii Preface xxvii About the Contributors xxxv Acknowledgment xlvii List of Abbreviations xlix 1 Introduction to PHM 1; Michael G. Pecht andMyeongsu Kang 1.1 Reliability and Prognostics 1 1.2 PHM for Electronics 3 1.3 PHM Approaches 6 1.3.1 PoF-Based Approach 6 1.3.1.1 Failure Modes, Mechanisms, and Effects Analysis (FMMEA) 7 1.3.1.2 Life-Cycle Load Monitoring 8 1.3.1.3 Data Reduction and Load Feature Extraction 10 1.3.1.4 Data Assessment and Remaining Life Calculation 12 1.3.1.5 Uncertainty Implementation and Assessment 13 1.3.2 Canaries 14 1.3.3 Data-Driven Approach 16 1.3.3.1 Monitoring and Reasoning of Failure Precursors 16 1.3.3.2 Data Analytics and Machine Learning 20 1.3.4 Fusion Approach 23 1.4 Implementation of PHM in a System of Systems 24 1.5 PHM in the Internet ofThings (IoT) Era 26 1.5.1 IoT-Enabled PHM Applications: Manufacturing 27 1.5.2 IoT-Enabled PHM Applications: Energy Generation 27 1.5.3 IoT-Enabled PHM Applications: Transportation and Logistics 28 1.5.4 IoT-Enabled PHM Applications: Automobiles 28 1.5.5 IoT-Enabled PHM Applications: Medical Consumer Products 29 1.5.6 IoT-Enabled PHM Applications:Warranty Services 29 1.5.7 IoT-Enabled PHM Applications: Robotics 30 1.6 Summary 30 References 30 2 Sensor Systems for PHM 39; Hyunseok Oh, Michael H. Azarian, Shunfeng Cheng, andMichael G. Pecht 2.1 Sensor and Sensing Principles 39 2.1.1 Thermal Sensors 40 2.1.2 Electrical Sensors 41 2.1.3 Mechanical Sensors 42 2.1.4 Chemical Sensors 42 2.1.5 Humidity Sensors 44 2.1.6 Biosensors 44 2.1.7 Optical Sensors 45 2.1.8 Magnetic Sensors 45 2.2 Sensor Systems for PHM 46 2.2.1 Parameters to be Monitored 47 2.2.2 Sensor System Performance 48 2.2.3 Physical Attributes of Sensor Systems 48 2.2.4 Functional Attributes of Sensor Systems 49 2.2.4.1 Onboard Power and Power Management 49 2.2.4.2 Onboard Memory and Memory Management 50 2.2.4.3 Programmable SamplingMode and Sampling Rate 51 2.2.4.4 Signal Processing Software 51 2.2.4.5 Fast and Convenient Data Transmission 52 2.2.5 Reliability 53 2.2.6 Availability 53 2.2.7 Cost 54 2.3 Sensor Selection 54 2.4 Examples of Sensor Systems for PHM Implementation 54 2.5 Emerging Trends in Sensor Technology for PHM 59 References 60 3 Physics-of-Failure Approach to PHM 61; Shunfeng Cheng, Nagarajan Raghavan, Jie Gu, Sony Mathew, and Michael G. Pecht 3.1 PoF-Based PHM Methodology 61 3.2 Hardware Configuration 62 3.3 Loads 63 3.4 Failure Modes, Mechanisms, and Effects Analysis (FMMEA) 64 3.4.1 Examples of FMMEA for Electronic Devices 68 3.5 Stress Analysis 71 3.6 Reliability Assessment and Remaining-Life Predictions 73 3.7 Outputs from PoF-Based PHM 77 3.8 Caution and Concerns in the Use of PoF-Based PHM 78 3.9 Combining PoF with Data-Driven Prognosis 80 References 81 4 Machine Learning: Fundamentals 85; Myeongsu Kang and Noel Jordan Jameson 4.1 Types of Machine Learning 85 4.1.1 Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning 86 4.1.2 Batch and Online Learning 88 4.1.3 Instance-Based and Model-Based Learning 89 4.2 Probability Theory in Machine Learning: Fundamentals 90 4.2.1 Probability Space and Random Variables 91 4.2.2 Distributions, Joint Distributions, and Marginal Distributions 91 4.2.3 Conditional Distributions 91 4.2.4 Independence 92 4.2.5 Chain Rule and Bayes Rule 92 4.3 Probability Mass Function and Probability Density Function 93 4.3.1 Probability Mass Function 93 4.3.2 Probability Density Function 93 4.4 Mean, Variance, and Covariance Estimation 94 4.4.1 Mean 94 4.4.2 Variance 94 4.4.3 Robust Covariance Estimation 95 4.5 Probability Distributions 96 4.5.1 Bernoulli Distribution 96 4.5.2 Normal Distribution 96 4.5.3 Uniform Distribution 97 4.6 Maximum Likelihood and Maximum A Posteriori Estimation 97 4.6.1 Maximum Likelihood Estimation 97 4.6.2 Maximum A Posteriori Estimation 98 4.7 Correlation and Causation 99 4.8 Kernel Trick 100 4.9 Performance Metrics 102 4.9.1 Diagnostic Metrics 102 4.9.2 Prognostic Metrics 105 References 107 5 Machine Learning: Data Pre-processing 111; Myeongsu Kang and Jing Tian 5.1 Data Cleaning 111 5.1.1 Missing Data Handling 111 5.1.1.1 Single-Value Imputation Methods 113 5.1.1.2 Model-Based Methods 113 5.2 Feature Scaling 114 5.3 Feature Engineering 116 5.3.1 Feature Extraction 116 5.3.1.1 PCA and Kernel PCA 116 5.3.1.2 LDA and Kernel LDA 118 5.3.1.3 Isomap 119 5.3.1.4 Self-Organizing Map (SOM) 120 5.3.2 Feature Selection 121 5.3.2.1 Feature Selection: FilterMethods 122 5.3.2.2 Feature Selection:WrapperMethods 124 5.3.2.3 Feature Selection: Embedded Methods 124 5.3.2.4 Advanced Feature Selection 125 5.4 Imbalanced Data Handling 125 5.4.1 SamplingMethods for Imbalanced Learning 126 5.4.1.1 Synthetic Minority Oversampling Technique 126 5.4.1.2 Adaptive Synthetic Sampling 126 5.4.1.3 Effect of SamplingMethods for Diagnosis 127 References 129 6 Machine Learning: Anomaly Detection 131; Myeongsu Kang 6.1 Introduction 131 6.2 Types of Anomalies 133 6.2.1 Point Anomalies 134 6.2.2 Contextual Anomalies 134 6.2.3 Collective Anomalies 135 6.3 Distance-Based Methods 136 6.3.1 MD Calculation Using an Inverse Matrix Method 137 6.3.2 MD Calculation Using a Gram–Schmidt Orthogonalization Method 137 6.3.3 Decision Rules 138 6.3.3.1 Gamma Distribution:Threshold Selection 138 6.3.3.2 Weibull Distribution:Threshold Selection 139 6.3.3.3 Box-Cox Transformation:Threshold Selection 139 6.4 Clustering-Based Methods 140 6.4.1 k-Means Clustering 141 6.4.2 Fuzzy c-Means Clustering 142 6.4.3 Self-Organizing Maps (SOMs) 142 6.5 Classification-Based Methods 144 6.5.1 One-Class Classification 145 6.5.1.1 One-Class Support Vector Machines 145 6.5.1.2 k-Nearest Neighbors 148 6.5.2 Multi-Class Classification 149 6.5.2.1 Multi-Class Support Vector Machines 149 6.5.2.2 Neural Networks 151 6.6 StatisticalMethods 153 6.6.1 Sequential Probability Ratio Test 154 6.6.2 Correlation Analysis 156 6.7 Anomaly Detection with No System Health Profile 156 6.8 Challenges in Anomaly Detection 158 References 159 7 Machine Learning: Diagnostics and Prognostics 163; Myeongsu Kang 7.1 Overview of Diagnosis and Prognosis 163 7.2 Techniques for Diagnostics 165 7.2.1 Supervised Machine Learning Algorithms 165 7.2.1.1 Naïve Bayes 165 7.2.1.2 Decision Trees 167 7.2.2 Ensemble Learning 169 7.2.2.1 Bagging 170 7.2.2.2 Boosting: AdaBoost 171 7.2.3 Deep Learning 172 7.2.3.1 Supervised Learning: Deep Residual Networks 173 7.2.3.2 Effect of Feature Learning-Powered Diagnosis 176 7.3 Techniques for Prognostics 178</p&g
• Edition: Second edition
• Publisher Details: Hoboken, New Jersey : John Wiley & Sons, Inc
• Publication Date: 2018
• Extent: 1 online resource
• Subjects: 621.381028; Electronic systems -- Testing; Electronic apparatus and appliances -- Testing; Electronic systems -- Maintenance and repair; Electronic apparatus and appliances -- Maintenance and repair; Automatic test equipment
• Languages: English
• ISBNs: 9781119515357
• Related ISBNs: 9781119515302
• Notes: Note: Description based on CIP data; resource not viewed.
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