Adaptive parameter selection variational mode decomposition based on a novel hybrid entropy and its applications in locomotive bearing diagnosis

Abstract

The emission signal of locomotive bearings is non-stationary, nonlinear and mixed with complex noise when operating online. The adaptive decomposition method is widely employed for denoising and feature extraction. Variational mode decomposition (VMD) is a powerful adaptive decomposition method, but the number of modes K and the penalty factor α are difficult to set in advance and the unreasonable parameter selection may probably bring faulty feature loss. To address this problem, a novel hybrid entropy, which includes the envelope fuzzy and dispersion characteristics of entropy, is proposed to describe the complexity and detect dynamic change of intrinsic mode functions component of VMD. Furthermore, an adaptive parameter selection variational mode decomposition (APSVMD) based on the novel hybrid entropy is presented to obtain the optimal parameter combination (K and α). Then the sensitive intrinsic mode function (IMF) can be chosen by novel hybrid entropy based on its new features. In order to verify the performance of the proposed diagnostic approach, the locomotive online running test was performed. The experimental results demonstrated that the APSVMD represents the signal modes explicitly and can process different types of signals. As a result, different kinds of bearings faults were diagnosed successfully.