Overview
- 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
- Includes supplementary material: sn.pub/extras
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Table of contents (8 chapters)
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Front Matter
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Back Matter
Keywords
About this book
• 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.
Authors and Affiliations
About the authors
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.
Bibliographic Information
Book Title: Knowledge-Driven Board-Level Functional Fault Diagnosis
Authors: Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu
DOI: https://doi.org/10.1007/978-3-319-40210-9
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2017
Hardcover ISBN: 978-3-319-40209-3Published: 29 August 2016
Softcover ISBN: 978-3-319-82054-5Published: 14 June 2018
eBook ISBN: 978-3-319-40210-9Published: 19 August 2016
Edition Number: 1
Number of Pages: XIII, 147
Number of Illustrations: 10 b/w illustrations, 65 illustrations in colour
Topics: Circuits and Systems, Data Mining and Knowledge Discovery, Electronics and Microelectronics, Instrumentation