A genetic algorithm for generating fuzzy classification rules
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Hopfield/ART-1 neural network-based fault detection and isolation
IEEE Transactions on Neural Networks
Review of fault diagnosis in control systems
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Evolutionary construction and adaptation of intelligent systems
Expert Systems with Applications: An International Journal
On-line adaptive clustering for process monitoring and fault detection
Expert Systems with Applications: An International Journal
A multivariate fuzzy system applied for outliers detection
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This work focuses on the design and implementation of a fuzzy inference system for fault detection and isolation (FDI) which can learn from example fault data, and the determination of a suitable optimisation strategy for the membership functions. A FDI system was developed which is based on adaptive fuzzy rules. A number of optimisation strategies were then applied; it was found that an evolutionary algorithm not only produced the best results but did so with relatively little processing effort and with excellent consistency. The adaptive fuzzy system, thus optimised, was tested against a neural network, which was trained to produce analogue outputs as an indication of fault magnitude. The fuzzy solution produced the best accuracy. We can conclude that an adaptive fuzzy inference system for FDI, using an evolutionary algorithm to learn from examples, can provide an accurate and readily comprehensible solution to diagnosing and evaluating fluid process plant faults.