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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.
List of contents
Fault Detection in Absence of Process Model. Fault Diagnosis in Expert Systems. In-Time Prognosis in Expert Systems for Health Monitoring. Predictive Maintenance in Industrial Systems. Meta-Heuristic Approaches in Industrial Processes. Fault Detection Based on Wavelet Signal Analysis by Means of Cultural Algorithms. Fault Diagnosis Based on Meta-Heuristic Strategies Applied to Deep Knowledge Approach. In-Time Prognosis Based on Particle Swarm Optimization. Predictive Maintenance Based on Extremal Optimization. Fault Detection on Elevator Systems. Fault Diagnosis in Industrial Systems. In-Time Prognosis in Home Healthcare Systems. Remote Building Maintenance.
Summary
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.
