Research on fault diagnosis method of electro-hydraulic system based on improved Radial Basis Function neural network

Aiming at the problems of complex fault diagnosis and slow processing speed of the current special vehicle electro-hydraulic system, this paper proposes an electro-hydraulic fault diagnosis system for special vehicle based on RBF neural network. The improved convolutional neural network is used to extract the original data features of the special vehicle electro-hydraulic system, and then the extracted data features are sent to the RBF neural network for training, which can greatly reduce the training time, and the extracted features are in the underfitting state at this time, thereby reducing the risk of overfitting. The experimental results on the test samples show that the improved system can quickly and accurately locate the fault, and give the corresponding expert maintenance advice, which has certain reference value for the health management of special vehicles.