At present, the large-scale railway maintenance equipment adopts a diesel engine as the main power plant. Therefore the diesel engine in the event of failure, will seriously affect the large-scale railway maintenance equipment of the normal work. Exploring advanced diesel engine condition monitoring and fault diagnosis technology and looking for practical and effective diesel engine fault diagnosis method, which has already become a research subject widely concerned by many experts at home and abroad. In this paper, genetic algorithm (GA) is used to optimize the parameters of radial basis function (RBF) neural network for diesel engine fault diagnosis, experimental results show the validity of this prediction method, and the accuracy of the proposed algorithm was verified by comparative.
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