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KnowledgeDriven BoardLevel Functional Fault Diagnosis,Used
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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 reasoningbased 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 reasoningbased, boardlevel diagnosis system design and presents the solutions and corresponding results that have emerged from leadingedge research in this domain. It covers topics ranging from highly accurate fault isolation, adaptive fault isolation, diagnosissystem robustness assessment, to system performance analysis and evaluation, knowledge discovery and knowledge transfer. With its emphasis on the above topics, the book provides an indepth and broad view of reasoningbased fault diagnosis system design. Explains and applies optimized techniques from the machinelearning 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 diagnosissystem development.
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This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.