Prognostics and Health Management of Electronics : Fundamentals, Machine Learning, and the Internet of Things

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. 

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.