Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu

eBook, PDF

Knowledge-Driven Board-Level Functional Fault Diagnosis (eBook, PDF)

Beschreibung

This book provides a comprehensive set of characterization, prediction, optimization, evaluation, and evolution techniques for a diagnosis system for fault isolation in large electronic systems. Readers with a background in electronics design or system engineering can use this book as a reference to derive insightful knowledge from data analysis and use this knowledge as guidance for designing reasoning-based diagnosis systems. Moreover, readers with a background in statistics or data analytics can use this book as a practical case study for adapting data mining and machine learning techniques to electronic system design and diagnosis. This book identifies the key challenges in reasoning-based, board-level diagnosis system design and presents the solutions and corresponding results that have emerged from leading-edge research in this domain. It covers topics ranging from highly accurate fault isolation, adaptive fault isolation, diagnosis-system robustness assessment, to system performance analysis and evaluation, knowledge discovery and knowledge transfer. With its emphasis on the above topics, the book provides an in-depth and broad view of reasoning-based fault diagnosis system design.

.Explains and applies optimized techniques from the machine-learning domain to solve the fault diagnosis problem in the realm of electronicsystem design and manufacturing;

.Demonstrates techniques based on industrial data and feedback from anactual manufacturing line;

.Discusses practical problems, including diagnosis accuracy, diagnosistime cost, evaluation of diagnosis system, handling of missing syndromesin diagnosis, and need for fast diagnosis-system development.

• Verlag: Springer International Publishing
• Seitenzahl: 147
• Erscheinungstermin: 19. August 2016
• Englisch
• ISBN-13: 9783319402109
• Artikelnr.: 46972422