A fault diagnosis method combined with LMD, sample entropy and energy ratio for roller bearings

Since the vibration signals of roller bearings are non-linear and non-stationary, the fault diagnosis of roller bearings is very difficult to determine. Characterized by the self-adaptive time-frequency, local mean decomposition (LMD) is suitable for analyzing this kind of complex signals. By using LMD method, vibration signals of roller bearings can be decomposed into a number of product functions (PFs) and a residual trend. In order to diagnose the fault of roller bearings, the PF components derived from LMD method are used to extract the features of fault signals. Considering the fact that sample entropy and energy ratio can reflect the regularity and characteristics of vibration signals to some extent, the two factors are chosen as PFs' feature vectors. Thus, a novel fault diagnosis method combining LMD method, sample entropy and energy ratio for roller bearings is put forward. By using the Support Vector Machine (SVM) classifier to make classification, the analysis results demonstrate that the proposed fault diagnosis and feature extraction method is effective.