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Book synopsis

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 electronic system design and manufacturing; - Demonstrates techniques based on industrial data and feedback from an actual manufacturing line; - Discusses practical problems, including diagnosis accuracy, diagnosis time cost, evaluation of diagnosis system, handling of missing syndromes in diagnosis, and need for fast diagnosis-system development. §

Book details

Book category Books in English Technology, engineering, agriculture Electronics & communications engineering Electronics engineering

• Full title: Knowledge-Driven Board-Level Functional Fault Diagnosis
• Author: Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu
• Language: English
• Binding: Hardback
• Number of pages: 147
• EAN: 9783319402093
• ISBN: 9783319402093
• ID: 10929142
• Publisher: Springer International Publishing AG
• Weight: 3731 g
• Dimensions: 235 × 155 × 11 mm
• Date of publishing: 19. August 2016

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