Mallak, Ahlam: Comprehensive machine and deep learning fault detection and classification approaches of industry 4.0 mechanical machineries: with application to a hydraulic [...]. 2021
Inhalt
- Acknowledgments
- Abstract
- Zusammenfassung
- Table of Contents
- List of Figures
- List of Tables
- List of Acronyms
- Chapter 1: Introduction
- 1. Motivation
- 2. Problem Statement
- 3. Research Questions
- 4. Our Contribution
- 5. Our Publications
- 6. Structure of the Dissertation
- Chapter 2: Conceptual and Theoretical Foundation
- 1. The Fourth Industrial Revolution
- 2. Hydraulic Systems Overview
- 3. Fault Types and Classifications
- 4. Fault Detection and Diagnosis (FDD)
- 5. Machine Learning Algorithms Taxonomy
- 6. Feature Selection Literature
- 7. k-means Clustering Literature
- 8. Relevant ML Classification Algorithms
- 9. Relevant DL Literature
- 10. Other Relevant Literature
- 11. Data Collection and Generation
- Chapter 3: Relevant Related Work
- 1. Supervised ML Approaches for FDD in Mechanical Machinery
- 2. Autoencoder Approaches for FDD in Mechanical Machinery
- 3. k-means for Feature Selection Related Work
- Chapter 4: Unsupervised Feature Selection Using Recursive k-Means Silhouette Elimination (RkSE): A Two-Scenario Case Study for Fault Classification of High-Dimensional Sensor Data
- 1. Chapter Overview
- 2. Recursive k-means Silhouette Elimination (RkSE): Method Overview
- 3. Analysis and Experimental Results
- Chapter 5: Sensor and Component FDD for Hydraulic Systems using Combined LSTM Autoencoder Detector and Diagnosis Classifiers
- Chapter 6: A Hybrid Approach: Dynamic Diagnostic Rules for Hydraulic Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest
- Chapter 7: Conclusions and Future Work
- References
- Appendix A: Ontology and Ontology Design
- Appendix B: Active Diagnosis and Repair Automotive (ADRA) Ontology
- Appendix C: SenGen: A Two-Phase Dynamic Simulation and Toolbox for Sensor Datasets and Case-Study Generation in Mobile Wireless Sensor Networks (MWSN)
- Appendix D: A Model-Based Approach: A Graph-Based FDD for IoT Systems Extracted from A Semantic Ontology