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.

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.