Neural Networks And Fuzzy Expert Systems In Fault Diagnosis

With production process becoming more and more complicated, how to improve the dependability and security of the large-scale equipment has already aroused great concern from people. At present, the representative intelligent methods based on the artificial neural network(ANN) and fuzzy system have been extensively adopted in fault diagnosis.In this paper the design ideas of the fault diagnosis system(FDS) and the basic principles of ANN and fuzzy logic system are described, and the basic methods in systematic structure, knowledge showing, knowledge acquisition and reasoning mechanism are analysed in detail. On the basis, an artificial neural network is integrated with fuzzy system for fault diagnosis. ANN detects the loop faults sources through the partial data measured, and outputs the fault degrees of corresponding loops. Fuzzy system detects the element faults through the preliminary diagnosis results obtained by ANN connected with other correlative values measured, and interprets the final results. The FDS combines the adaptive learning diagnosis procedure of the ANN and the transparent knowledge representation of the fuzzy system, and simplifies the process of obtaining the ANN's learning sample and establishing the fuzzy inference rules. Through the fault diagnosis simulation of a hot nitric acid cooling system, it has been proven that the fault diagnosis method is very valid.