Advertisement

Knowledge-Driven Board-Level Functional Fault Diagnosis

  • Fangming Ye
  • Zhaobo Zhang
  • Krishnendu Chakrabarty
  • Xinli Gu

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu
    Pages 1-21
  3. Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu
    Pages 23-42
  4. Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu
    Pages 43-59
  5. Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu
    Pages 61-78
  6. Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu
    Pages 79-93
  7. Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu
    Pages 95-119
  8. Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu
    Pages 121-142
  9. Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu
    Pages 143-146
  10. Back Matter
    Pages 147-147

About this book

Introduction

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.


Keywords

Functional Fault Diagnosis Intelligent Fault Diagnosis Data-Driven Design of Fault Diagnosis Resilient system design Design, test, and repair of 3D-Integrated Circuits

Authors and affiliations

  • Fangming Ye
    • 1
  • Zhaobo Zhang
    • 2
  • Krishnendu Chakrabarty
    • 3
  • Xinli Gu
    • 4
  1. 1.Qualcomm Atheros, Inc.San JoseUSA
  2. 2.Huawei TechnologiesSanta ClaraUSA
  3. 3.Dept. Electrical & Comp. EngineerinDuke UniversityDurhamUSA
  4. 4.Huawei TechnologiesSanta ClaraUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-40210-9
  • Copyright Information Springer International Publishing Switzerland 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-40209-3
  • Online ISBN 978-3-319-40210-9
  • Buy this book on publisher's site
Industry Sectors
Automotive
Electronics
IT & Software
Telecommunications
Energy, Utilities & Environment
Aerospace
Oil, Gas & Geosciences
Engineering