Knowledge-Driven Board-Level Functional Fault Diagnosis

1. Front Matter

   Pages i-xiii

   PDF

2. Introduction

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

   Pages 1-21

3. Diagnosis Using Support Vector Machines (SVM)

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

   Pages 23-42

4. Diagnosis Using Multiple Classifiers and Majority-Weighted Voting (WMV)

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

   Pages 43-59

5. Adaptive Diagnosis Using Decision Trees (DT)

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

   Pages 61-78

6. Information-Theoretic Syndrome and Root-Cause Evaluation

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

   Pages 79-93

7. Handling Missing Syndromes

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

   Pages 95-119

8. Knowledge Discovery and Knowledge Transfer

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

   Pages 121-142

9. Conclusions

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

   Pages 143-146

10. Back Matter

    Pages 147-147

    PDF

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.

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

• Qualcomm Atheros, Inc., San Jose, USA

  Fangming Ye

• Huawei Technologies, Santa Clara, USA

  Zhaobo Zhang, Xinli Gu

• Dept. Electrical & Comp. Engineerin, Duke University, Durham, USA

  Krishnendu Chakrabarty

Fangming Ye is a Staff Engineer at Huawei Technologies, with particular research interests in machine learning, data mining, resilient system design, and diagnosis system for board-level faults.

Zhaobo Zhang is a Staff Engineer at Huawei Technologies, specializing in Data analysis and machine learning, Network reliability, Application design, Flow standardization, diagnosis automation, and memory test.

Krishnendu Chakrabarty is the William H. Younger Distinguished Professor of Engineering in the Department of Electrical and Computer Engineering and Professor of Computer Science at Duke University. He is a recipient of the National Science Foundation Early Faculty (CAREER) award, the Office of Naval Research Young Investigator award, the Humboldt Research Award from the Alexander von Humboldt Foundation, Germany, the IEEE Transactions on CAD Donald O. Pederson Best Paper award (2015), and 11 best paper awards at major IEEE conferences. Heis also a recipient of the IEEE Computer Society Technical Achievement Award (2015) and the Distinguished Alumnus Award from the Indian Institute of Technology, Kharagpur (2014). Prof. Chakrabarty is a Hans Fischer Senior Fellow at the Institute for Advanced Studies, Technical University of Munich, Germany.

Prof. Chakrabarty’s current research projects include: testing and design-for-testability of integrated circuits and system; digital microfluidics, biochips, and cyberphysical systems; optimization of enterprise systems and smart manufacturing. He is a Fellow of ACM, a Fellow of IEEE, and a Golden Core Member of the IEEE Computer Society. Prof. Chakrabarty served as the Editor-in-Chief of IEEE Design & Test of Computers during 2010-2012 and ACM Journal on Emerging Technologies in Computing Systems during 2010-2015. Currently he serves as the Editor-in-Chief of IEEE Transactions on VLSI Systems. 

Xinli Gu is a Senior Director at Huawei Technologies, where he leads design solution for network product quality and reliability. He also had 12-year experiences with Cisco Systems, responsible for product testability and manufacturing quality at corporate level. 

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