EEMD method and WNN for fault diagnosis of locomotive roller bearings

Abstract

The ensemble empirical mode decomposition (EEMD) can overcome the mode mixing problem of the empirical mode decomposition (EMD) and therefore provide more precise decomposition results. Wavelet neural network (WNN) possesses the advantages of both wavelet transform and artificial neural networks. This paper combines the merits of EEMD and WNN to propose an automated and effective fault diagnosis method of locomotive roller bearings. First, the vibration signals captured from the locomotive roller bearings are preprocessed by EEMD method and intrinsic mode functions (IMFs) are produced. Second, a kurtosis based method is presented and used to select the sensitive IMF. Third, time- and frequency-domain features are extracted from the sensitive IMF, its frequency spectrum and its envelope spectrum. Finally, these features are fed into WNN to identify the bearing health conditions. The diagnosis results show that the proposed method enables the identification of the single faults in the bearings and at the same time the recognition of the fault severities and the compound faults.