Knowledge-driven board-level functional fault diagnosis

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...

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Bibliographic Details
Main Authors: Ye, Fangming, (Author), Zhang, Zhaobo, (Author), Chakrabarty, Krishnendu, (Author), Gu, Xinli, (Author)
Format: eBook
Language: English
Published: Switzerland : Springer, [2016]
Subjects:
Fault location (Engineering)
Fault tolerance (Engineering)
elektronické knihy
electronic books
ISBN: 9783319402109
9783319402093
Physical Description: 1 online resource (xiii, 147 pages) : illustrations (some color)

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Table of contents

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245 0 0 |a Knowledge-driven board-level functional fault diagnosis /  |c Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu. 
264 1 |a Switzerland :  |b Springer,  |c [2016] 
264 4 |c ©2017 
300 |a 1 online resource (xiii, 147 pages) :  |b illustrations (some color) 
336 |a text  |b txt  |2 rdacontent 
337 |a počítač  |b c  |2 rdamedia 
338 |a online zdroj  |b cr  |2 rdacarrier 
505 0 |a Preface; Acknowledgments; Contents; 1 Introduction; 1.1 Introduction to Manufacturing Test; 1.1.1 System and Tests; 1.1.2 Testing in the Manufacturing Line; 1.2 Introduction to Board-Level Diagnosis; 1.2.1 Review of State-of-the-Art; 1.2.2 Automation in Diagnosis System; 1.2.3 New Directions Enabled by Machine Learning; 1.2.4 Challenges and Opportunities; 1.3 Outline of Book; References; 2 Diagnosis Using Support Vector Machines (SVM); 2.1 Background and Chapter Highlights; 2.2 Diagnosis Using Support Vector Machines; 2.2.1 Support Vector Machines; 2.2.2 SVM Diagnosis Flow. 
505 8 |a 2.3 Multi-kernel Support Vector Machines and Incremental Learning2.3.1 Multi-kernel Support Vector Machines; 2.3.2 Incremental Learning; 2.4 Results; 2.4.1 Evaluation of MK-SVM-Based Diagnosis System; 2.4.2 Evaluation of Incremental SVM-Based Diagnosis System; 2.4.3 Evaluation of Incremental MK-SVM-Based Diagnosis System; 2.5 Chapter Summary; References; 3 Diagnosis Using Multiple Classifiers and Majority-Weighted Voting (WMV); 3.1 Background and Chapter Highlights; 3.2 Artificial Neural Networks (ANN); 3.2.1 Architecture of ANNs; 3.2.2 Demonstration of ANN-Based Diagnosis System. 
505 8 |a 3.3 Comparison Between ANNs and SVMs3.4 Diagnosis Using Weighted-Majority Voting; 3.4.1 Weighted-Majority Voting; 3.4.2 Demonstration of WMV-Based Diagnosis System; 3.5 Results; 3.5.1 Evaluation of ANNs-Based Diagnosis System; 3.5.2 Evaluation of SVMs-Based Diagnosis System; 3.5.3 Evaluation of WMV-Based Diagnosis System; 3.6 Chapter Summary; References; 4 Adaptive Diagnosis Using Decision Trees (DT); 4.1 Background and Chapter Highlights; 4.2 Decision Trees; 4.2.1 Training of Decision Trees; 4.2.2 Example of DT-Based Training and Diagnosis; 4.3 Diagnosis Using Incremental Decision Trees. 
505 8 |a 4.3.1 Incremental Tree Node4.3.2 Addition of a Case; 4.3.3 Ensuring the Best Splitting; 4.3.4 Tree Transposition; 4.4 Diagnosis Flow Based on Incremental Decision Trees; 4.5 Results; 4.5.1 Evaluation of DT-Based Diagnosis System; 4.5.2 Evaluation of Incremental DT-Based Diagnosis System; 4.6 Chapter Summary; References; 5 Information-Theoretic Syndrome and Root-Cause Evaluation; 5.1 Background and Chapter Highlights; 5.2 Evaluation Methods for Diagnosis Systems; 5.2.1 Subset Selection for Syndromes Analysis; 5.2.2 Class-Relevance Statistics; 5.3 Evaluation and Enhancement Framework. 
505 8 |a 5.3.1 Evaluation and Enhancement Procedure5.3.2 An Example of the Proposed Framework; 5.4 Results; 5.4.1 Demonstration of Syndrome Analysis; 5.4.2 Demonstration of Root-Cause Analysis; 5.5 Chapter Summary; References; 6 Handling Missing Syndromes; 6.1 Background and Chapter Highlights; 6.2 Methods to Handle Missing Syndromes; 6.2.1 Missing-Syndrome-Tolerant Fault Diagnosis Flow; 6.2.2 Missing-Syndrome-Preprocessing Methods; 6.2.3 Feature Selection; 6.3 Results; 6.3.1 Evaluation of Label Imputation; 6.3.2 Evaluation of Feature Selection in Handling Missing Syndromes. 
504 |a Includes bibliographical references and index. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a 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. 
590 |a SpringerLink  |b Springer Complete eBooks 
650 0 |a Fault location (Engineering) 
650 0 |a Fault tolerance (Engineering) 
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655 9 |a electronic books  |2 eczenas 
700 1 |a Ye, Fangming,  |e author. 
700 1 |a Zhang, Zhaobo,  |e author. 
700 1 |a Chakrabarty, Krishnendu,  |e author. 
700 1 |a Gu, Xinli,  |e author. 
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