An indispensable guide for engineers and data scientists in design, testing, operation, manufacturing, and maintenance, Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and the Internet of Things offers a comprehensive roadmap to the current challenges and available opportunities in 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. It also delves into assessing the cost and benefits of prognostic implementations, developing novel methods for in situ monitoring of products and systems in actual life-cycle conditions, enabling condition-based (predictive) maintenance, increasing system availability through an extension of maintenance cycles and/or timely repair actions, obtaining knowledge of load history for future design, qualification, and root cause analysis, reducing the occurrence of no fault found (NFF), and subtracting life-cycle costs of equipment from reduction in inspection costs, downtime, and inventory.

Additionally, this valuable resource explains how to understand statistical techniques and machine learning methods used for diagnostics and prognostics. By leveraging insights into IoT, machine learning, and risk assessment, electrical engineers, data scientists, and design engineers can gain a deeper understanding of the synergies between these fields. Perfect for professionals seeking to enhance their knowledge in PHM, this book is essential reading for those working on the cutting edge of electronic system reliability and maintenance.

Published by Wiley-IEEE Press on August 21, 2018, with ISBN-13: 978-1119515357, Prognostics and Health Management of Electronics provides a wealth of information to support the research and development of PHM in various applications.

Read more about this groundbreaking work on Prognostics and Health Management for electronics today!