Composite fault diagnosis for rolling bearing based on parameter-optimized VMD

Abstract

Variational mode decomposition (VMD) is a recently introduced adaptive signal analysis method, which is widely used in fault diagnosis of rotating machinery due to its excellent noise reduction performance. But it is often used to realize a single fault diagnosis of the bearing. In this paper, a feasible solution based on the parameter-optimized VMD is introduced to realize the composite fault diagnosis of bearing whose fault components are located in different frequency bands, and without combining with other methods. First, the kurtosis value of the envelope signal is used as an indicator to determine the optimal mode number. Then, after the mode number is determined, the penalty factor is optimized by the same indicator. Then, the original signal is decomposed into the sum of a certain number of intrinsic mode functions (IMFs), each IMF has a different frequency band. Then, the IMFs are sorted in descending order of the envelope signal kurtosis value, and the IMFs with significantly smaller indicator values are removed. Finally, in accordance with the sequence of the reserved IMF components, the power spectrum analysis of the envelope is performed on each IMF to realize the feature extraction of bearing composite faults. Simulation analysis and actual case analysis prove the robustness and feasibility of the presented solution.