Automatic Monitoring, Fault Diagnostics, and Maintenance : Using Meta-Heuristic Optimization

Based on practical field experience and several case studies, this book presents state-of-the-art artificial intelligence techniques, validating their efficiency and accuracy in real-world industrial applications. The authors show how a meta-heuristic approach can enhance automatic fault monitoring in man-made systems. For each phase of fault monitoring, they propose a meta-heuristic approach that is tested experimentally in an actual industrial environment. While the proposed methods are validated on specific industrial problems, their applicability is wide and general.

Preface

Chapter 1 : Fault Detection in Absence of Process Model
Chapter 2 : Fault Diagnosis in Expert Systems
Chapter 3 : In-Time Prognosis in Expert Systems for Health Monitoring
Chapter 4 : Predictive Maintenance in Industrial Systems
Chapter 5 : Meta-Heuristic Approaches in Industrial Processes
Chapter 6 : Fault Detection Based on Wavelet Signal Analysis by Means of Cultural Algorithms
Chapter 7 : Fault Diagnosis Based on Meta-Heuristic Strategies Applied to Deep Knowledge Approach
Chapter 8 : In-Time Prognosis Based on Particle Swarm Optimization
Chapter 9 : Predictive Maintenance Based on Extremal Optimization
Chapter 10 : Fault Detection on Elevator Systems
Chapter 11 : Fault Diagnosis in Industrial Systems
Chapter 12 : In-Time Prognosis in Home Healthcare Systems
Chapter 13 : Remote Building Maintenance

Index