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Knowledge-Driven Board-Level Functional Fault Diagnosis 1st ed. 2017 [Kõva köide]

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  • Formaat: Hardback, 147 pages, kõrgus x laius: 235x155 mm, kaal: 3731 g, 65 Illustrations, color; 10 Illustrations, black and white; XIII, 147 p. 75 illus., 65 illus. in color., 1 Hardback
  • Ilmumisaeg: 29-Aug-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319402099
  • ISBN-13: 9783319402093
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Knowledge-Driven Board-Level Functional Fault Diagnosis 1st ed. 2017Suurem pilt
  • Formaat: Hardback, 147 pages, kõrgus x laius: 235x155 mm, kaal: 3731 g, 65 Illustrations, color; 10 Illustrations, black and white; XIII, 147 p. 75 illus., 65 illus. in color., 1 Hardback
  • Ilmumisaeg: 29-Aug-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319402099
  • ISBN-13: 9783319402093
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319402093.html
Märksõnad:
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 pe

rformance 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. 

Introduction.- Diagnosis System Design for Higher Accuracy.- Adaptive Diagnosis Process.- Handling Missing Syndromes.- Information-Theoretic Evaluation of Diagnosis System.- Knowledge Discover and Knowledge Transfer.- Conclusion.
1 Introduction
1(22)
1.1 Introduction to Manufacturing Test
1(5)
1.1.1 System and Tests
1(2)
1.1.2 Testing in the Manufacturing Line
3(3)
1.2 Introduction to Board-Level Diagnosis
6(11)
1.2.1 Review of State-of-the-Art
7(3)
1.2.2 Automation in Diagnosis System
10(3)
1.2.3 New Directions Enabled by Machine Learning
13(2)
1.2.4 Challenges and Opportunities
15(2)
1.3 Outline of Book
17(6)
References
18(5)
2 Diagnosis Using Support Vector Machines (SVM)
23(20)
2.1 Background and
Chapter Highlights
24(1)
2.2 Diagnosis Using Support Vector Machines
25(4)
2.2.1 Support Vector Machines
25(3)
2.2.2 SVM Diagnosis Flow
28(1)
2.3 Multi-kernel Support Vector Machines and Incremental Learning
29(5)
2.3.1 Multi-kernel Support Vector Machines
29(2)
2.3.2 Incremental Learning
31(3)
2.4 Results
34(7)
2.4.1 Evaluation of MK-SVM-Based Diagnosis System
36(1)
2.4.2 Evaluation of Incremental SVM-Based Diagnosis System
37(2)
2.4.3 Evaluation of Incremental MK-SVM-Based Diagnosis System
39(2)
2.5
Chapter Summary
41(2)
References
42(1)
3 Diagnosis Using Multiple Classifiers and Majority-Weighted Voting (WMV)
43(18)
3.1 Background and
Chapter Highlights
44(1)
3.2 Artificial Neural Networks (ANN)
45(4)
3.2.1 Architecture of ANNs
46(2)
3.2.2 Demonstration of ANN-Based Diagnosis System
48(1)
3.3 Comparison Between ANNs and SVMs
49(1)
3.4 Diagnosis Using Weighted-Majority Voting
49(2)
3.4.1 Weighted-Majority Voting
49(2)
3.4.2 Demonstration of WMV-Based Diagnosis System
51(1)
3.5 Results
51(7)
3.5.1 Evaluation of ANNs-Based Diagnosis System
52(3)
3.5.2 Evaluation of SVMs-Based Diagnosis System
55(1)
3.5.3 Evaluation of WMV-Based Diagnosis System
56(2)
3.6
Chapter Summary
58(3)
References
59(2)
4 Adaptive Diagnosis Using Decision Trees (DT)
61(18)
4.1 Background and
Chapter Highlights
62(1)
4.2 Decision Trees
63(4)
4.2.1 Training of Decision Trees
63(2)
4.2.2 Example of DT-Based Training and Diagnosis
65(2)
4.3 Diagnosis Using Incremental Decision Trees
67(5)
4.3.1 Incremental Tree Node
67(1)
4.3.2 Addition of a Case
68(2)
4.3.3 Ensuring the Best Splitting
70(1)
4.3.4 Tree Transposition
71(1)
4.4 Diagnosis Flow Based on Incremental Decision Trees
72(2)
4.5 Results
74(4)
4.5.1 Evaluation of DT-Based Diagnosis System
75(2)
4.5.2 Evaluation of Incremental DT-Based Diagnosis System
77(1)
4.6
Chapter Summary
78(1)
References
78(1)
5 Information-Theoretic Syndrome and Root-Cause Evaluation
79(16)
5.1 Background and
Chapter Highlights
80(2)
5.2 Evaluation Methods for Diagnosis Systems
82(3)
5.2.1 Subset Selection for Syndromes Analysis
82(2)
5.2.2 Class-Relevance Statistics
84(1)
5.3 Evaluation and Enhancement Framework
85(2)
5.3.1 Evaluation and Enhancement Procedure
85(1)
5.3.2 An Example of the Proposed Framework
86(1)
5.4 Results
87(5)
5.4.1 Demonstration of Syndrome Analysis
89(1)
5.4.2 Demonstration of Root-Cause Analysis
89(3)
5.5
Chapter Summary
92(3)
References
93(2)
6 Handling Missing Syndromes
95(26)
6.1 Background and
Chapter Highlights
95(2)
6.2 Methods to Handle Missing Syndromes
97(9)
6.2.1 Missing-Syndrome-Tolerant Fault Diagnosis Flow
98(1)
6.2.2 Missing-Syndrome-Preprocessing Methods
98(7)
6.2.3 Feature Selection
105(1)
6.3 Results
106(12)
6.3.1 Evaluation of Label Imputation
107(2)
6.3.2 Evaluation of Feature Selection in Handling Missing Syndromes
109(1)
6.3.3 Comparison of Different Missing-Syndrome Handling Methods
110(4)
6.3.4 Evaluation of Training Time
114(4)
6.4
Chapter Summary
118(3)
References
118(3)
7 Knowledge Discovery and Knowledge Transfer
121(22)
7.1 Background and
Chapter Highlights
121(2)
7.2 Overview of Knowledge Discovery and Transfer Framework
123(1)
7.3 Knowledge-Discovery Method
124(4)
7.4 Knowledge-Transfer Method
128(5)
7.5 Results
133(8)
7.5.1 Evaluation of Knowledge-Discover Method
138(1)
7.5.2 Evaluation of Knowledge-Transfer Method
138(1)
7.5.3 Evaluation of Hybrid Method
139(2)
7.6
Chapter Summary
141(2)
References
142(1)
8 Conclusions
143(4)
Index 147
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. Chakrabartys 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.