A new model for bearing fault diagnosis based on optimized variational mode decomposition correlation coefficient weight threshold denoising and entropy feature fusion

For the bearing fault diagnosis in small sample cases, a new model for signal denoising and entropy feature fusion (EFF) based on the wild horse optimizer (WHO) optimized variational mode decomposition (VMD) and correlation coefficient weight threshold (CCWT) is proposed (WHO–VMD–CCWT–EFF). For signal denoising, we first take the power spectrum entropy as the fitness function, and the WHO is used to optimize VMD parameters. Secondly, IMFs with correlation coefficient values less than 0.2 are removed and the correlation coefficient values as weights are applied to the corresponding IMF components, and then reconstruct them. Then, the refined composite multiscale dispersion entropy (RCMDE), refined composite multiscale fluctuation dispersion entropy (RCMFDE), refined composite multivariate generalized multiscale fuzzy entropy (RCmvMFE), refined composite multivariate generalized multiscale sample entropy (RCmvMSE), and multiscale permutation entropy (MPE) of the signal are calculated and fused. Finally, the Fisher discriminant classifier is used as the model for fault diagnosis. The proposed model achieves an accuracy of over 99% in 12 single working conditions and 30 multiple working conditions experiments using the case western reserve university (CWRU) dataset and the Paderborn dataset. Compared with existing feature fusion methods, the WHO–VMD–CCWT–EFF model only integrates five selected features, and can achieve accurate diagnosis of bearing faults in small sample experiments with 42 different artificial and real damages. This indicates that the model has good generalization ability between different datasets and working conditions.