Prognostics and health management of electronics : fundamentals, machine learning, and internet of things /
edited by Michael Pecht, Ph.D., PE, Myeongsu Kang, Ph.D.
- Second edition
- 1 online resource (800 pages)
ABOUT THE AUTHOR 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.
Includes bibliographical references and index.
TABLE OF 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
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
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.