Title: BP Neural Network Based on Genetic Algorithm for Fault Diagnosis of Rolling Bearing
Abstract: There are two main weak points of the classical BP algorithm which is easily stacked into local minima and takes much more times. In this paper, the bearing fault intelligent diagnosis method has been studied based on BP neural network combined with genetic algorithm. In genetic algorithm training of BP network weights, the real number encoding was applied. For GA optimal of locomotive and car bearing fault diagnosis BP network weights, optimal population was selected as third individual, with the use of fitness proportionate selection and discrete crossover as well as average mutation. Comparing GA and traditional gradient algorithm for weights optimal of the BP neural network, GA takes much little optimal time. The efficiency of research work mentioned above has been shown by numerical simulation and practical bearing fault diagnosis.
Publication Year: 1999
Publication Date: 1999-01-01
Language: en
Type: article
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