A hydraulic fault diagnosis method based on sliding-window spectrum feature and deep belief network

The vibration signal of hydraulic system contains abundant state information, so vibration testing technology is an effective way to realize the fault diagnosis of hydraulic system. However, the mapping relation between signal characteristic and system state is complex and the expression meaning of characteristic is obscure, which brings a great challenge to the hydraulic fault diagnosis. The DBN, a newly proposed deep learning model, has an advantage of autonomously learning and reasoning. And it is good at studying the concealed representation of data and highlighting the feature expression. So, it is contributive to deal with the problems of large capacity data like high dimension, redundancy, and nonlinear etc. Therefore, DBN is chosen as the fault diagnosis method in this paper. Meanwhile, given that the difficulty in feature extraction of hydraulic vibration signal and the important influence of input feature vector to the diagnosing of DBN, a fast and effectively feature extraction method based on sliding-window spectrum feature (SWSF) is proposed. It is effective in remaining the integrity of feature, avoiding the risking of relative shifting of characteristic spectrum, and decreasing the dimensions of feature vector. The experimental results demonstrate that the combination of SWSF and DBN is a fast and effective approach to realize the fault diagnosis of hydraulic system.