Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and the Internet of Things, 2nd EditionКНИГИ » АППАРАТУРА
Название: Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and the Internet of Things, 2nd Edition Автор: Michael G. Pecht, Myeongsu Kang Издательство: Wiley-IEEE Press ISBN: 1119515335 Год: 2018 Страниц: 800 Язык: английский Формат: pdf (true) Размер: 24.9 MB
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.
Prognostics and health management (PHM) is a multifaceted discipline that protects the integrity of components, products, and systems of systems by avoiding unanticipated problems that can lead to performance deficiencies and adverse effects on safety. More specifically, prognostics is the process of predicting a system’s remaining useful life (RUL).
List of Contributors xxiii Preface xxvii 1 Introduction to PHM 1 2 Sensor Systems for PHM 39 3 Physics-of-Failure Approach to PHM 61 4 Machine Learning: Fundamentals 85 5 Machine Learning: Data Pre-processing 111 6 Machine Learning: Anomaly Detection 131 7 Machine Learning: Diagnostics and Prognostics 163 8 Uncertainty Representation, Quantification, and Management in Prognostics 193 9 PHM Cost and Return on Investment 221 10 Valuation and Optimization of PHM-Enabled Maintenance Decisions 261 11 Health and Remaining Useful Life Estimation of Electronic Circuits 279 12 PHM-Based Qualification of Electronics 329 13 PHM of Li-ion Batteries 349 14 PHM of Light-Emitting Diodes 377 15 PHM in Healthcare 431 16 PHM of Subsea Cables 451 17 Connected Vehicle Diagnostics and Prognostics 479 18 The Role of PHM at Commercial Airlines 503 19 PHM Software for Electronics 535 20 eMaintenance 559 21 Predictive Maintenance in the IoT Era 589 22 Analysis of PHM Patents for Electronics 613 23 A PHM Roadmap for Electronics-Rich Systems 64 Appendix A Commercially Available Sensor Systems for PHM 691 Appendix B Journals and Conference Proceedings Related to PHM 721 Appendix C Glossary of Terms and Definitions 725 Index 731
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Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and the Internet of Things, 2nd Edition 
