- Full Description
Presenting the latest developments and research results on fault diagnosis approaches using computational intelligence methodologies, this book opens with a review of the state-of-the-art before focusing on various theoretical aspects of computational intelligence methodologies applied to real-world fault diagnosis problems. Chapters deal with topics such as fuzzy sets applications to fault diagnosis, neural network based fault diagnosis applications and neuro-fuzzy techniques for fault diagnosis. The last chapter considers the problem of diagnosing large scale complex systems using local agents which, can be implemented using computational intelligence based fault diagnosis techniques. Several case studies are used. This book presents the most recent concerns and research results in industrial fault diagnosis using intelligent techniques, and will be of interest to application engineers/technologists, graduates and researchers wishing to apply these techniques, as well as build up a foundation for further study.
- Table of Contents
Table of Contents
- Computational intelligence methodologies in fault diagnosis: Review and state of the art.
- A fuzzy logic approach to gas path diagnostics in aero
- Fault detection and isolation of industrial processes using optimized fuzzy models.
- A fuzzy classification technique applied to fault diagnosis.
- statistical reasoning in fault diagnosis.
- Artificial neural networks in fault diagnosis: A gas turbine scenario.
- stage neural networks based classifier system for fault diagnosis.
- Soft computing models for fault diagnosis of conductive flow systems.
- Fault diagnosis in a power generation plant using a neural fuzzy system with rule extraction.
- Fuzzy neural networks applied to fault diagnosis.
- Causal models for distributed fault diagnosis of complex systems.
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