Includes bibliographical references and index.

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 -- 7.3.1 Regression Analysis 178 -- 7.3.1.1 Linear Regression 178 -- 7.3.1.2 Polynomial Regression 180 -- 7.3.1.3 Ridge Regression 181 -- 7.3.1.4 LASSO Regression 182 -- 7.3.1.5 Elastic Net Regression 183 -- 7.3.1.6 k-Nearest Neighbors Regression 183 -- 7.3.1.7 Support Vector Regression 184 -- 7.3.2 Particle Filtering 185 -- 7.3.2.1 Fundamentals of Particle Filtering 186 -- 7.3.2.2 Resampling Methods - A Review 187 -- References 189 -- 8 Uncertainty Representation, Quantification, and Management in Prognostics 193 /Shankar Sankararaman -- 8.1 Introduction 193 -- 8.2 Sources of Uncertainty in PHM 196 -- 8.3 Formal Treatment of Uncertainty in PHM 199 -- 8.3.1 Problem 1: Uncertainty Representation and Interpretation 199 -- 8.3.2 Problem 2: Uncertainty Quantification 199 -- 8.3.3 Problem 3: Uncertainty Propagation 200 -- 8.3.4 Problem 4: Uncertainty Management 200 -- 8.4 Uncertainty Representation and Interpretation 200 -- 8.4.1 Physical Probabilities and Testing-Based Prediction 201 -- 8.4.1.1 Physical Probability 201 -- 8.4.1.2 Testing-Based Life Prediction 201.

8.4.1.3 Confidence Intervals 202 -- 8.4.2 Subjective Probabilities and Condition-Based Prognostics 202 -- 8.4.2.1 Subjective Probability 202 -- 8.4.2.2 Subjective Probabilities in Condition-Based Prognostics 203 -- 8.4.3 Why is RUL Prediction Uncertain? 203 -- 8.5 Uncertainty Quantification and Propagation for RUL Prediction 203 -- 8.5.1 Computational Framework for Uncertainty Quantification 204 -- 8.5.1.1 Present State Estimation 204 -- 8.5.1.2 Future State Prediction 205 -- 8.5.1.3 RUL Computation 205 -- 8.5.2 RUL Prediction: An Uncertainty Propagation Problem 206 -- 8.5.3 Uncertainty PropagationMethods 206 -- 8.5.3.1 Sampling-Based Methods 207 -- 8.5.3.2 AnalyticalMethods 209 -- 8.5.3.3 Hybrid Methods 209 -- 8.5.3.4 Summary of Methods 209 -- 8.6 Uncertainty Management 210 -- 8.7 Case Study: Uncertainty Quantification in the Power System of an Unmanned Aerial Vehicle 211 -- 8.7.1 Description of the Model 211 -- 8.7.2 Sources of Uncertainty 212 -- 8.7.3 Results: Constant Amplitude Loading Conditions 213 -- 8.7.4 Results: Variable Amplitude Loading Conditions 214 -- 8.7.5 Discussion 214 -- 8.8 Existing Challenges 215 -- 8.8.1 Timely Predictions 215 -- 8.8.2 Uncertainty Characterization 216 -- 8.8.3 Uncertainty Propagation 216 -- 8.8.4 Capturing Distribution Properties 216 -- 8.8.5 Accuracy 216 -- 8.8.6 Uncertainty Bounds 216 -- 8.8.7 Deterministic Calculations 216 -- 8.9 Summary 217 -- References 217 -- 9 PHM Cost and Return on Investment 221 /Peter Sandborn, ChrisWilkinson, Kiri Lee Sharon, Taoufik Jazouli, and Roozbeh Bakhshi -- 9.1 Return on Investment 221 -- 9.1.1 PHM ROI Analyses 222 -- 9.1.2 Financial Costs 224 -- 9.2 PHM Cost-Modeling Terminology and Definitions 225 -- 9.3 PHM Implementation Costs 226 -- 9.3.1 Nonrecurring Costs 226 -- 9.3.2 Recurring Costs 227 -- 9.3.3 Infrastructure Costs 228 -- 9.3.4 Nonmonetary Considerations and Maintenance Culture 228 -- 9.4 Cost Avoidance 229 -- 9.4.1 Maintenance Planning Cost Avoidance 231 -- 9.4.2 Discrete-Event Simulation Maintenance PlanningModel 232.

9.4.3 Fixed-Schedule Maintenance Interval 233 -- 9.4.4 Data-Driven (Precursor to Failure Monitoring) Methods 233 -- 9.4.5 Model-Based (LRU-Independent)Methods 234 -- 9.4.6 Discrete-Event Simulation Implementation Details 236 -- 9.4.7 Operational Profile 237 -- 9.5 Example PHM Cost Analysis 238 -- 9.5.1 Single-Socket Model Results 239 -- 9.5.2 Multiple-Socket Model Results 241 -- 9.6 Example Business Case Construction: Analysis for ROI 246 -- 9.7 Summary 255 -- References 255 -- 10 Valuation and Optimization of PHM-Enabled Maintenance Decisions 261 /Xin Lei, Amir Reza Kashani-Pour, Peter Sandborn, and Taoufik Jazouli -- 10.1 Valuation and Optimization of PHM-Enabled Maintenance Decisions for an Individual System 262 -- 10.1.1 A PHM-Enabled Predictive Maintenance OptimizationModel for an Individual System 263 -- 10.1.2 Case Study: Optimization of PHM-Enabled Maintenance Decisions for an Individual System (Wind Turbine) 265 -- 10.2 Availability 268 -- 10.2.1 The Business of Availability: Outcome-Based Contracts 269 -- 10.2.2 Incorporating Contract Terms into Maintenance Decisions 270 -- 10.2.3 Case Study: Optimization of PHM-Enabled Maintenance Decisions for Systems (Wind Farm) 270 -- 10.3 Future Directions 272 -- 10.3.1 Design for Availability 272 -- 10.3.2 Prognostics-BasedWarranties 275 -- 10.3.3 Contract Engineering 276 -- References 277 -- 11 Health and Remaining Useful Life Estimation of Electronic Circuits 279 /Arvind Sai Sarathi Vasan and Michael G. Pecht -- 11.1 Introduction 279 -- 11.2 RelatedWork 281 -- 11.2.1 Component-Centric Approach 281 -- 11.2.2 Circuit-Centric Approach 282 -- 11.3 Electronic Circuit Health Estimation Through Kernel Learning 285 -- 11.3.1 Kernel-Based Learning 285 -- 11.3.2 Health Estimation Method 286 -- 11.3.2.1 Likelihood-Based Function for Model Selection 288 -- 11.3.2.2 Optimization Approach for Model Selection 289 -- 11.3.3 Implementation Results 292 -- 11.3.3.1 Bandpass Filter Circuit 293 -- 11.3.3.2 DC-DC Buck Converter System 300.

11.4 RUL Prediction Using Model-Based Filtering 306 -- 11.4.1 Prognostics Problem Formulation 306 -- 11.4.2 Circuit DegradationModeling 307 -- 11.4.3 Model-Based Prognostic Methodology 310 -- 11.4.4 Implementation Results 313 -- 11.4.4.1 Low-Pass Filter Circuit 313 -- 11.4.4.2 Voltage Feedback Circuit 315 -- 11.4.4.3 Source of RUL Prediction Error 320 -- 11.4.4.4 Effect of First-Principles-Based Modeling 320 -- 11.5 Summary 322 -- References 324 -- 12 PHM-Based Qualification of Electronics 329 /Preeti S. Chauhan -- 12.1 Why is Product Qualification Important? 329 -- 12.2 Considerations for Product Qualification 331 -- 12.3 Review of Current Qualification Methodologies 334 -- 12.3.1 Standards-Based Qualification 334 -- 12.3.2 Knowledge-Based or PoF-Based Qualification 337 -- 12.3.3 Prognostics and Health Management-Based Qualification 340 -- 12.3.3.1 Data-Driven Techniques 340 -- 12.3.3.2 Fusion Prognostics 343 -- 12.4 Summary 345 -- References 346 -- 13 PHM of Li-ion Batteries 349 /Saurabh Saxena, Yinjiao Xing, andMichael G. Pecht -- 13.1 Introduction 349 -- 13.2 State of Charge Estimation 351 -- 13.2.1 SOC Estimation Case Study I 352 -- 13.2.1.1 NN Model 353 -- 13.2.1.2 Training and Testing Data 354 -- 13.2.1.3 Determination of the NN Structure 355 -- 13.2.1.4 Training and Testing Results 356 -- 13.2.1.5 Application of Unscented Kalman Filter 357 -- 13.2.2 SOC Estimation Case Study II 357 -- 13.2.2.1 OCV-SOC-T Test 358 -- 13.2.2.2 Battery Modeling and Parameter Identification 359 -- 13.2.2.3 OCV-SOC-T Table for Model Improvement 360 -- 13.2.2.4 Validation of the Proposed Model 362 -- 13.2.2.5 Algorithm Implementation for Online Estimation 362 -- 13.3 State of Health Estimation and Prognostics 365 -- 13.3.1 Case Study for Li-ion Battery Prognostics 366 -- 13.3.1.1 Capacity DegradationModel 366 -- 13.3.1.2 Uncertainties in Battery Prognostics 368 -- 13.3.1.3 Model Updating via Bayesian Monte Carlo 368 -- 13.3.1.4 SOH Prognostics and RUL Estimation 369 -- 13.3.1.5 Prognostic Results 371.

13.4 Summary 371 -- References 372 -- 14 PHM of Light-Emitting Diodes 377 /Moon-Hwan Chang, Jiajie Fan, Cheng Qian, and Bo Sun -- 14.1 Introduction 377 -- 14.2 Review of PHM Methodologies for LEDs 378 -- 14.2.1 Overview of Available Prognostic Methods 378 -- 14.2.2 Data-DrivenMethods 379 -- 14.2.2.1 Statistical Regression 379 -- 14.2.2.2 Static Bayesian Network 381 -- 14.2.2.3 Kalman Filtering 382 -- 14.2.2.4 Particle Filtering 383 -- 14.2.2.5 Artificial Neural Network 384 -- 14.2.3 Physics-Based Methods 385 -- 14.2.4 LED System-Level Prognostics 387 -- 14.3 Simulation-Based Modeling and Failure Analysis for LEDs 388 -- 14.3.1 LED Chip-LevelModeling and Failure Analysis 389 -- 14.3.1.1 Electro-optical Simulation of LED Chip 389 -- 14.3.1.2 LED Chip-Level Failure Analysis 393 -- 14.3.2 LED Package-Level Modeling and Failure Analysis 395 -- 14.3.2.1 Thermal and Optical Simulation for Phosphor-Converted White LED Package 395 -- 14.3.2.2 LED Package-Level Failure Analysis 397 -- 14.3.3 LED System-LevelModeling and Failure Analysis 399 -- 14.4 Return-on-Investment Analysis of Applying Health Monitoring to LED Lighting Systems 401 -- 14.4.1 ROI Methodology 403 -- 14.4.2 ROI Analysis of Applying System Health Monitoring to LED Lighting Systems 406 -- 14.4.2.1 Failure Rates and Distributions for ROI Simulation 407 -- 14.4.2.2 Determination of Prognostics Distance 410 -- 14.4.2.3 IPHM, CPHM, and Cu Evaluation 412 -- 14.4.2.4 ROI Evaluation 417 -- 14.5 Summary 419 -- References 420 -- 15 PHM in Healthcare 431 /Mary Capelli-Schellpfeffer,Myeongsu Kang, andMichael G. Pecht -- 15.1 Healthcare in the United States 431 -- 15.2 Considerations in Healthcare 432 -- 15.2.1 Clinical Consideration in ImplantableMedical Devices 432 -- 15.2.2 Considerations in Care Bots 433 -- 15.3 Benefits of PHM 438 -- 15.3.1 Safety Increase 439 -- 15.3.2 Operational Reliability Improvement 440 -- 15.3.3 Mission Availability Increase 440 -- 15.3.4 System’s Service Life Extension 441 -- 15.3.5 Maintenance Effectiveness Increase 441.

15.4 PHM of ImplantableMedical Devices 442 -- 15.5 PHM of Care Bots 444 -- 15.6 Canary-Based Prognostics of Healthcare Devices 445 -- 15.7 Summary 447 -- References 447 -- 16 PHM of Subsea Cables 451 /David Flynn, Christopher Bailey, Pushpa Rajaguru,Wenshuo Tang, and Chunyan Yin -- 16.1 Subsea Cable Market 451 -- 16.2 Subsea Cables 452 -- 16.3 Cable Failures 454 -- 16.3.1 Internal Failures 455 -- 16.3.2 Early-Stage Failures 455 -- 16.3.3 External Failures 455 -- 16.3.4 Environmental Conditions 455 -- 16.3.5 Third-Party Damage 456 -- 16.4 State-of-the-Art Monitoring 457 -- 16.5 Qualifying and Maintaining Subsea Cables 458 -- 16.5.1 Qualifying Subsea Cables 458 -- 16.5.2 Mechanical Tests 458 -- 16.5.3 Maintaining Subsea Cables 459 -- 16.6 Data-Gathering Techniques 460 -- 16.7 Measuring theWear Behavior of Cable Materials 461 -- 16.8 Predicting Cable Movement 463 -- 16.8.1 Sliding Distance Derivation 463 -- 16.8.2 Scouring Depth Calculations 465 -- 16.9 Predicting Cable Degradation 466 -- 16.9.1 Volume Loss due to Abrasion 466 -- 16.9.2 Volume Loss due to Corrosion 466 -- 16.10 Predicting Remaining Useful Life 468 -- 16.11 Case Study 471 -- 16.12 Future Challenges 471 -- 16.12.1 Data-Driven Approach for Random Failures 471 -- 16.12.2 Model-Driven Approach for Environmental Failures 473 -- 16.12.2.1 Fusion-Based PHM 473 -- 16.12.2.2 Sensing Techniques 474 -- 16.13 Summary 474 -- References 475 -- 17 Connected Vehicle Diagnostics and Prognostics 479 /Yilu Zhang and Xinyu Du -- 17.1 Introduction 479 -- 17.2 Design of an Automatic Field Data Analyzer 481 -- 17.2.1 Data Collection Subsystem 482 -- 17.2.2 Information Abstraction Subsystem 482 -- 17.2.3 Root Cause Analysis Subsystem 482 -- 17.2.3.1 Feature-Ranking Module 482 -- 17.2.3.2 Relevant Feature Set Selection 484 -- 17.2.3.3 Results Interpretation 486 -- 17.3 Case Study: CVDP for Vehicle Batteries 486 -- 17.3.1 Brief Background of Vehicle Batteries 486 -- 17.3.2 Applying AFDA for Vehicle Batteries 488 -- 17.3.3 Experimental Results 489.

Contents xvii -- 17.3.3.1 Information Abstraction 490 -- 17.3.3.2 Feature Ranking 490 -- 17.3.3.3 Interpretation of Results 495 -- 17.4 Summary 498 -- References 499 -- 18 The Role of PHM at Commercial Airlines 503 /RhondaWalthall and Ravi Rajamani -- 18.1 Evolution of Aviation Maintenance 503 -- 18.2 Stakeholder Expectations for PHM 506 -- 18.2.1 Passenger Expectations 506 -- 18.2.2 Airline/Operator/Owner Expectations 507 -- 18.2.3 Airframe Manufacturer Expectations 509 -- 18.2.4 Engine Manufacturer Expectations 510 -- 18.2.5 System and Component Supplier Expectations 511 -- 18.2.6 MRO Organization Expectations 512 -- 18.3 PHM Implementation 513 -- 18.3.1 SATAA 513 -- 18.4 PHM Applications 517 -- 18.4.1 Engine Health Management (EHM) 517 -- 18.4.1.1 History of EHM 518 -- 18.4.1.2 EHM Infrastructure 519 -- 18.4.1.3 Technologies Associated with EHM 520 -- 18.4.1.4 The Future 523 -- 18.4.2 Auxiliary Power Unit (APU) Health Management 524 -- 18.4.3 Environmental Control System (ECS) and Air Distribution Health Monitoring 525 -- 18.4.4 Landing System Health Monitoring 526 -- 18.4.5 Liquid Cooling System Health Monitoring 526 -- 18.4.6 Nitrogen Generation System (NGS) Health Monitoring 527 -- 18.4.7 Fuel Consumption Monitoring 527 -- 18.4.8 Flight Control Actuation Health Monitoring 528 -- 18.4.9 Electric Power System Health Monitoring 529 -- 18.4.10 Structural Health Monitoring (SHM) 529 -- 18.4.11 Battery Health Management 531 -- 18.5 Summary 532 -- References 533 -- 19 PHM Software for Electronics 535 /Noel Jordan Jameson,Myeongsu Kang, and Jing Tian -- 19.1 PHM Software: CALCE Simulation Assisted Reliability Assessment 535 -- 19.2 PHM Software: Data-Driven 540 -- 19.2.1 Data Flow 541 -- 19.2.2 Master Options 542 -- 19.2.3 Data Pre-processing 543 -- 19.2.4 Feature Discovery 545 -- 19.2.5 Anomaly Detection 546 -- 19.2.6 Diagnostics/Classification 548 -- 19.2.7 Prognostics/Modeling 552 -- 19.2.8 Challenges in Data-Driven PHM Software Development 554 -- 19.3 Summary 557.

20 eMaintenance 559 /Ramin Karim, Phillip Tretten, and Uday Kumar -- 20.1 From Reactive to Proactive Maintenance 559 -- 20.2 The Onset of eMaintenance 560 -- 20.3 MaintenanceManagement System 561 -- 20.3.1 Life-cycle Management 562 -- 20.3.2 eMaintenance Architecture 564 -- 20.4 Sensor Systems 564 -- 20.4.1 Sensor Technology for PHM 565 -- 20.5 Data Analysis 565 -- 20.6 Predictive Maintenance 566 -- 20.7 Maintenance Analytics 567 -- 20.7.1 Maintenance Descriptive Analytics 568 -- 20.7.2 Maintenance Analytics and eMaintenance 568 -- 20.7.3 Maintenance Analytics and Big Data 568 -- 20.8 Knowledge Discovery 570 -- 20.9 Integrated Knowledge Discovery 571 -- 20.10 User Interface for Decision Support 572 -- 20.11 Applications of eMaintenance 572 -- 20.11.1 eMaintenance in Railways 572 -- 20.11.1.1 Railway Cloud: Swedish Railway Data 573 -- 20.11.1.2 Railway Cloud: Service Architecture 573 -- 20.11.1.3 Railway Cloud: Usage Scenario 574 -- 20.11.2 eMaintenance in Manufacturing 574 -- 20.11.3 MEMS Sensors for Bearing Vibration Measurement 576 -- 20.11.4 Wireless Sensors for Temperature Measurement 576 -- 20.11.5 Monitoring Systems 576 -- 20.11.6 eMaintenance Cloud and Servers 578 -- 20.11.7 Dashboard Managers 580 -- 20.11.8 Alarm Servers 580 -- 20.11.9 Cloud Services 581 -- 20.11.10 Graphic User Interfaces 583 -- 20.12 Internet Technology and Optimizing Technology 585 -- References 586 -- 21 Predictive Maintenance in the IoT Era 589 /Rashmi B. Shetty -- 21.1 Background 589 -- 21.1.1 Challenges of a Maintenance Program 590 -- 21.1.2 Evolution of Maintenance Paradigms 590 -- 21.1.3 Preventive Versus Predictive Maintenance 592 -- 21.1.4 P-F Curve 592 -- 21.1.5 Bathtub Curve 594 -- 21.2 Benefits of a Predictive Maintenance Program 595 -- 21.3 Prognostic Model Selection for Predictive Maintenance 596 -- 21.4 Internet ofThings 598 -- 21.4.1 Industrial IoT 598 -- 21.5 Predictive Maintenance Based on IoT 599 -- 21.6 Predictive Maintenance Usage Cases 600 -- 21.7 Machine Learning Techniques for Data-Driven Predictive Maintenance 600.

21.7.1 Supervised Learning 602 -- 21.7.2 Unsupervised Learning 602 -- 21.7.3 Anomaly Detection 602 -- 21.7.4 Multi-class and Binary Classification Models 603 -- 21.7.5 Regression Models 604 -- 21.7.6 Survival Models 604 -- 21.8 Best Practices 604 -- 21.8.1 Define Business Problem and QuantitativeMetrics 605 -- 21.8.2 Identify Assets and Data Sources 605 -- 21.8.3 Data Acquisition and Transformation 606 -- 21.8.4 Build Models 607 -- 21.8.5 Model Selection 607 -- 21.8.6 Predict Outcomes and Transform into Process Insights 608 -- 21.8.7 Operationalize and Deploy 609 -- 21.8.8 Continuous Monitoring 609 -- 21.9 Challenges in a Successful Predictive Maintenance Program 610 -- 21.9.1 Predictive Maintenance Management Success Key Performance Indicators (KPIs) 610 -- 21.10 Summary 611 -- References 611 -- 22 Analysis of PHM Patents for Electronics 613 /Zhenbao Liu, Zhen Jia, Chi-Man Vong, Shuhui Bu, andMichael G. Pecht -- 22.1 Introduction 613 -- 22.2 Analysis of PHM Patents for Electronics 616 -- 22.2.1 Sources of PHM Patents 616 -- 22.2.2 Analysis of PHM Patents 617 -- 22.3 Trend of Electronics PHM 619 -- 22.3.1 Semiconductor Products and Computers 619 -- 22.3.2 Batteries 622 -- 22.3.3 Electric Motors 626 -- 22.3.4 Circuits and Systems 629 -- 22.3.5 Electrical Devices in Automobiles and Airplanes 631 -- 22.3.6 Networks and Communication Facilities 634 -- 22.3.7 Others 636 -- 22.4 Summary 638 -- References 639 -- 23 A PHM Roadmap for Electronics-Rich Systems 64 /Michael G. Pecht -- 23.1 Introduction 649 -- 23.2 Roadmap Classifications 650 -- 23.2.1 PHM at the Component Level 651 -- 23.2.1.1 PHM for Integrated Circuits 652 -- 23.2.1.2 High-Power Switching Electronics 652 -- 23.2.1.3 Built-In Prognostics for Components and Circuit Boards 653 -- 23.2.1.4 Photo-Electronics Prognostics 654 -- 23.2.1.5 Interconnect andWiring Prognostics 656 -- 23.2.2 PHM at the System Level 657 -- 23.2.2.1 Legacy Systems 657 -- 23.2.2.2 Environmental and OperationalMonitoring 659 -- 23.2.2.3 LRU to Device Level 659.

23.2.2.4 Dynamic Reconfiguration 659 -- 23.2.2.5 System Power Management and PHM 660 -- 23.2.2.6 PHM as Knowledge Infrastructure for System Development 660 -- 23.2.2.7 Prognostics for Software 660 -- 23.2.2.8 PHM for Mitigation of Reliability and Safety Risks 661 -- 23.2.2.9 PHM in Supply Chain Management and Product Maintenance 662 -- 23.3 Methodology Development 663 -- 23.3.1 Best Algorithms 664 -- 23.3.1.1 Approaches to Training 667 -- 23.3.1.2 Active Learning for Unlabeled Data 667 -- 23.3.1.3 Sampling Techniques and Cost-Sensitive Learning for Imbalanced Data 668 -- 23.3.1.4 Transfer Learning for Knowledge Transfer 668 -- 23.3.1.5 Internet ofThings and Big Data Analytics 669 -- 23.3.2 Verification and Validation 670 -- 23.3.3 Long-Term PHM Studies 671 -- 23.3.4 PHM for Storage 671 -- 23.3.5 PHM for No-Fault-Found/Intermittent Failures 672 -- 23.3.6 PHM for Products Subjected to Indeterminate Operating Conditions 673 -- 23.4 Nontechnical Barriers 674 -- 23.4.1 Cost, Return on Investment, and Business Case Development 674 -- 23.4.2 Liability and Litigation 676 -- 23.4.2.1 Code Architecture: Proprietary or Open? 676 -- 23.4.2.2 Long-Term Code Maintenance and Upgrades 676 -- 23.4.2.3 False Alarms, Missed Alarms, and Life-Safety Implications 677 -- 23.4.2.4 Warranty Restructuring 677 -- 23.4.3 Maintenance Culture 677 -- 23.4.4 Contract Structure 677 -- 23.4.5 Role of Standards Organizations 678 -- 23.4.5.1 IEEE Reliability Society and PHM Efforts 678 -- 23.4.5.2 SAE PHM Standards 678 -- 23.4.5.3 PHM Society 679 -- 23.4.6 Licensing and Entitlement Management 680 -- References 680 -- Appendix A Commercially Available Sensor Systems for PHM 691 -- A.1 SmartButton - ACR Systems 691 -- A.2 OWL 400 - ACR Systems 693 -- A.3 SAVERTM 3X90 - Lansmont Instruments 695 -- A.4 G-Link®-LXRS®- LORD MicroStrain®Sensing Systems 697 -- A.5 V-Link®-LXRS®- LORD MicroStrain Sensing Systems 699 -- A.6 3DM-GX4-25TM - LORD MicroStrain Sensing Systems 702 -- A.7 IEPE-LinkTM-LXRS®- LORD MicroStrain Sensing Systems 704.

A.8 ICHM®20/20 - Oceana Sensor 706 -- A.9 EnvironmentalMonitoring System 200TM - Upsite Technologies 708 -- A.10 S2NAP®- RLWInc. 710 -- A.11 SR1 Strain Gage Indicator - Advance Instrument Inc. 712 -- A.12 P3 Strain Indicator and Recorder - Micro-Measurements 714 -- A.13 Airscale Suspension-BasedWeighing System - VPG Inc. 716 -- A.14 Radio Microlog - Transmission Dynamics 718 -- Appendix B Journals and Conference Proceedings Related to PHM 721 -- B.1 Journals 721 -- B.2 Conference Proceedings 722 -- Appendix C Glossary of Terms and Definitions 725 -- Index 731.

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AN INDISPENSABLE GUIDE FOR ENGINEERS AND DATA SCIENTISTS IN DESIGN, TESTING, OPERATION, MANUFACTURING, AND MAINTENANCE A road map to the current challenges and available opportunities for the research and development of Prognostics and Health Management "PHM", this important work covers all areas of electronics and explains how to: . assess methods for damage estimation of components and systems due to field loading conditions. assess the cost and benefits of prognostic implementations. develop novel methods for in situ monitoring of products and systems in actual life-cycle conditions. enable condition-based "predictive" maintenance. increase system availability through an extension of maintenance cycles and/or timely repair actions. obtain knowledge of load history for future design, qualification, and root cause analysis. reduce the occurrence of no fault found "NFF". subtract life-cycle costs of equipment from reduction in inspection costs, downtime, and inventory Prognostics and Health Management of Electronics also explains how to understand statistical techniques and machine learning methods used for diagnostics and prognostics. Using this valuable resource, electrical engineers, data scientists, and design engineers will be able to fully grasp the synergy between IoT, machine learning, and risk assessment.

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