Knowledge-driven board-level functional fault diagnosis / Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu.
Contributor(s): Chakrabarty, Krishnendu,, autor | Gu, Xinli,, autor | Ye, Fangming,, autor | Zhang, Zhaobo,, autor
Material type:
E-bookPublisher: Switzerland : Springer, [2016]Copyright date: 2017Description: 1 recurso en línea (xiii, 147 páginas) : ilustraciones (algunas a color).ISBN: 3319402102; 9783319402109.Subject: Diagnóstico de fallos
Online resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|
LIBRO-E NO PRÉSTAMO
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Madrid Digital Acceso Electrónico (UEM) | Ciencias e Ingeniería | TA169.6 K569 2016 EB (Browse shelf(Opens below)) | Acceso electrónico | eBook.20022120 |
SpringerLink Springer Engineering eBooks 2017 English+International
Incluye referencias bibliográficas e índice
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
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
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
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
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
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