Fault Diagnosis of Complicated Machinery System Based on Genetic Algorithm and Fuzzy RBF Neural Network
Compared with traditional Back Propagation (BP) neural network, the advantages of fuzzy neural network in fault diagnosis are analyzed. A new diagnosis method based on genetic algorithm (GA) and fuzzy Radial Basis Function (RBF) neural network is presented for complicated machinery system. Fuzzy membership functions are obtained by using RBF neural network, and then genetic algorithm is applied to train fuzzy RBF neural network. The trained fuzzy RBF neural network is used for fault diagnosis of ship main power system. Diagnostic results indicate that the method is of good generalization performance and expansibility. It can significantly improve the diagnostic precision.
KeywordsFault Diagnosis Back Propagation Radial Basis Function Neural Network Radial Basis Function Network Fuzzy Neural Network
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