Details

Prognostics and Health Management of Electronics


Prognostics and Health Management of Electronics

Fundamentals, Machine Learning, and the Internet of Things
Wiley - IEEE 1. Aufl.

von: Michael G. Pecht, Myeongsu Kang

141,52 €

Verlag: Wiley-IEEE Press
Format: PDF
Veröffentl.: 15.08.2018
ISBN/EAN: 9781119515302
Sprache: englisch
Anzahl Seiten: 800

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Beschreibungen

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. 
List of Contributors xxiii Preface xxvii About the Contributors xxxv Acknowledgment xlvii List of Abbreviations xlix 1 Introduction to PHM 1Michael 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 39Hyunseok 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 61Shunfeng 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 85Myeongsu 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 111Myeongsu 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 131Myeongsu 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 163Myeongsu 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 193Shankar 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 221Peter 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 261Xin 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 279Arvind 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 329Preeti 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 349Saurabh 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 377Moon-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 431Mary 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 451David 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 479Yilu 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 503RhondaWalthall 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 535Noel 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 559Ramin 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 589Rashmi 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 613Zhenbao 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 64Michael 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
MICHAEL G. PECHT, PHD, is Chair Professor in Mechanical Engineering and Professor in Applied Mathematics, Statistics and Scientific Computation at the University of Maryland, USA. He is the Founder and Director of the Center for Advanced Life Cycle Engineering (CALCE) at the University of Maryland, USA, which is funded by more than 150 leading electronics companies. Dr. Pecht is an IEEE, ASME, SAE, and IMAPS Fellow and serves as editor-in-chief of IEEE Access. He has written more than 30 books, 700 technical articles, and has 8 patents. MYEONGSU KANG, PHD, is currently a Research Associate at the Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, USA. His expertise is in data analytics, machine learning, system modeling, and statistics for prognostics and systems health management. He has authored/coauthored more than 60 publications in leading journals and conference proceedings.
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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