Research of slew bearing signal de-noising based on multi-scale principal component analysis and EEMD

In order to extract the fault signal better, a new denoising method based on multi-scale principal component analysis (MSPCA) and the ensemble empirical mode decomposition (EEMD) were proposed. Then a new intrinsic mode functions (IMFs) selection strategy was proposed, which combined the merits of ensemble empirical mode decomposition (EEMD) and principal component analysis (PCA). Finally, vibration signal was reconstructed by the selected IMFs. In order to test the performance of the proposed denoising method, a comparison of the denoising method based on EEMD-kurtosis criterion and EEMD-correlation coefficient criterion was studied. The proposed method based on MSPCA and EEMD was validated by the simulated signals and practical fault signals of slewing bearing. The results show that the method for vibration signal filtering is more effective than other the two denoising methods. It can more effective to improve the signal to noise ratio (S_NR) and extract fault characteristic information. Hence, it has powerful value for engineering application.