Analysis of hydraulic system’s weak fault based on variable parameter multi-scale permutation entropy and deep belief network

Abstract

Aiming at automatic recognition of hydraulic system’s weak fault, an analysis method based on variable parameter multi-scale permutation entropy (VPMPE) and deep belief network (DBN) is proposed in this paper. The external vibration signals of experimental hydraulic equipment in normal state and three different leakage state (slight, moderate and severe) are taken as research object. By the proposed method, experiment signals are first processed to obtain their multi-scale permutation entropy in different conditions by changing the embedding dimension m and the scale factor s, then the multi-scale permutation entropy under different m and s are combined to form feature vectors, and lastly the DBN classifier is used to identify and analyze the testing samples. Verification test shows that the proposed method has good effects on hydraulic system’s weak fault, which can accurately judge whether there is leakage fault and measure the fault severity.