Study on fault diagnosis of data fussion in hydraulic pump

Directing to the dispersiveness and faintness of failure characteristics of hydraulic pump, this paper presented the fault diagnosis method based on data fussions through measuring the vibration signals and pressure signals. On the basis of failure mechanism analysis of slipper loosing, this paper executed the wavelet analysis to cancel the interference existed in the measured signals. In order to improve the efficiency of fault diagnosis, the paper utilized Principal Component Analysis (PCA) and detective verification to decouple the failure eigenvectors. Inducting the reset eigenvectors into BP neural network, this work realized the multiple failure diagnosis with improved algorithm. The experimental results indicate that the method is content.