Method Research Of Fault Diagnosis Based On Fuzzy Nerual Network And Genetic Algorithm

A new method of fault diagnosis is the main content of this paper. Concretely, Fuzzy Theory, Artificial Neural Network (ANN) and Genetic Algorithm (GA) and their application in equipment fault diagnosis are discussed in detail.In Fuzzy Theory, the characteristic parameters of equipment fault can be fuzzied by degree of membership, and then the fault diagnosis can be processed by the fuzzy inference. This fuzzy method is realized by the Fuzzy Inference System Toolbox of MATLAB.The characteristics of ANN indicate that it is suitable for the fault diagnosis. The popular BP network is adopted as the application model. Input data which are from fuzzy characteristic parameters are trained and simulated. The fault diagnosis of Fuzzy ANN is realized by the Neural Network Toolbox of MATLAB. The result is proved valid, but also proved easy to be trapped in the local extremum.In GA, with the weight and threshold of ANN encoded, then the initial population randomized, and the selection, crossover and mutation operated, the population can be optimized till requirement satisfied. The global search ability of GA covers the shortage of BP network, and save the training time. GA is realized by the Genetic Algorithm Toolbox developed by University of Sheffield, UK.A refrigeration system is cited here. The chosen fault characteristic parameters are fuzzied, and the weight and threshold of ANN are optimized, then the diagnosis is accomplished by BP network. The method of fault diagnosis based on fuzzy neural network and genetic algorithm is proved valid, and valuable in the academic and engineering application.