Application of wavelet entropy and grey relational analysis in fault diagnosis of hydraulic system of engineering machinery

The working environment of engineering machinery is complex. The signal of the equipment in operation is characterized by non-stationary and complex variability. In this paper, the wavelet transform is used to extract the non-stationary fault signals of the hydraulic system of the equipment. At the same time, a fault classification method based on the multi wavelet entropy feature extraction is proposed with the information entropy. The fault features are extracted quickly and accurately. The grey correlation analysis method is used to identify the fault, and the remote fault is set up. Diagnosis platform. The results show that the method can improve the support and accuracy of the fault diagnosis, and provides a feasible new method for the fault diagnosis of the engineering machinery.