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(Ebook) Knowledge-Driven Board-Level Functional Fault Diagnosis by Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu (auth.) ISBN 9783319402093, 9783319402109, 3319402099, 3319402102

  • SKU: EBN-5675212
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Instant download (eBook) Knowledge-Driven Board-Level Functional Fault Diagnosis after payment.
Authors:Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu (auth.)
Pages:154 pages.
Year:2017
Editon:1
Publisher:Springer International Publishing
Language:english
File Size:8.81 MB
Format:pdf
ISBNS:9783319402093, 9783319402109, 3319402099, 3319402102
Categories: Ebooks

Product desciption

(Ebook) Knowledge-Driven Board-Level Functional Fault Diagnosis by Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu (auth.) ISBN 9783319402093, 9783319402109, 3319402099, 3319402102

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.
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