Rolling Bearing Fault Diagnosis Based on Weighted Variational Mode Decomposition and Cyclic Spectrum Slice Energy

Abstract

As the main parts of rotating machinery, rolling bearing is prone to failure due to its harsh working environment. Aiming at the problem that the early fault features of a rolling bearing are easily submerged by noise and difficult to extract, a fault diagnosis method based on weighted variational mode decomposition (WVMD) and cyclic spectrum slice energy (CSSE) is proposed. Firstly, the signal is decomposed into intrinsic mode functions (IMFs) by VMD and the sparsity is used to measure the amount of information contained in each IMF, and all IMFs are weighted and reconstructed to suppress the noise interference components in the signal. Secondly, the advantage of the CSSE which can accurately mediate the fault information is used to analyze the reconstructed signal, and then the fault characteristic frequency of the reconstructed signal is extracted. Finally, the bearing simulation signal and outer ring fault signal are used to verify that the proposed diagnosis method can effectively extract the early fault features of rolling bearing.