Abstract:Aiming at the fault diagnosis problem of hydraulic pump, a method that based on the combination of Local characteristicscale decomposition (LCD), Fuzzy Entropy and SOM Neural Network was proposed. Firstly, the vibration signal of hydraulic pump was decomposed with LCD into several Intrinsic scale components (ISC). Secondly, the first few ISC components, which contained main fault information, were chosen by correlation coefficient analysis with the original signal and then the fuzzy entropy of these components was calculated to compose characteristic matrix. Finally, the characteristic entropy matrix used to train the SOM neural network to recognize different fault types was ensured. The experiment results of fault diagnosis of hydraulic pump show that the method can classify typical fault types of hydraulic pump exactly, and has certain superiority. By comparing with the classfied results of BP neural network, the superiority of the SOM neural network in fault feature classification is confirmed.