A novel data-driven fault diagnosis method based on VMD-RCMFE-DDMA-BASSVM model for rolling bearings

Condition monitoring and fault diagnosis of bearings play an important role in the safe operation of equipment and can reduce maintenance costs. In this paper, a novel data-driven bearing fault diagnosis model is developed. First, the variable modal decomposition method is applied for denoising and recombination to reduce noise interference. Next, the refined composite multi-scale fuzzy entropy is used to extract features from the recombined signal. After that, discriminant diffusion maps analysis is utilized to compress the high-dimensional features into the low-dimensional space and remove the interference of redundant features. Finally, the beetle antennae search support vector machine is adopted for fault classification. The proposed method is applied to the fault diagnosis of wind turbine bearings under various operating conditions, and the experimental results show that the proposed method can accurately and effectively identify various faults.