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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:
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.
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.
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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