Computational Intelligence in Fault Diagnosis [electronic resource] /
Palade, Vasile.editor.
Bocaniala, Cosmin Danut.editor.
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 moving on to focus 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, in particular, can be implemented using computational intelligence based fault diagnosis techniques. Several case studies are used including applying computational intelligence techniques to aero-engines fault diagnosis and diagnosing faults to various industrial devices, such as a flow control valve and a hydraulic installation in a rolling mill plant. Computational Intelligence in Fault Diagnosis 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, enhance their knowledge, as well as build up a foundation for further study.
Description based upon print version of record.
Includes bibliographical references and index.
Computational Intelligence Methodologies in Fault Diagnosis: Review and State of the Art -- A Fuzzy Logic Approach to Gas Path Diagnostics in Aero-engines -- Fault Detection and Isolation of Industrial Processes Using Optimized Fuzzy Models -- A Fuzzy Classification Technique Applied to Fault Diagnosis -- Fuzzy-Statistical Reasoning in Fault Diagnosis -- Artificial Neural Networks in Fault Diagnosis: A Gas Turbine Scenario -- Two-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.
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 moving on to focus 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, in particular, can be implemented using computational intelligence based fault diagnosis techniques. Several case studies are used including applying computational intelligence techniques to aero-engines fault diagnosis and diagnosing faults to various industrial devices, such as a flow control valve and a hydraulic installation in a rolling mill plant. Computational Intelligence in Fault Diagnosis 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, enhance their knowledge, as well as build up a foundation for further study.
Optical data processing.
Computers.
Logic design.
Artificial intelligence.
Computer simulation.
Computer software—Reusability.
Image Processing and Computer Vision.
Theory of Computation.
Logic Design.
Artificial Intelligence.
Simulation and Modeling.
Performance and Reliability.
Advanced Information and Knowledge Processing