Multi-scale fault frequency extraction method based on EEMD for slewing bearing fault diagnosis

In view of the large low-speed slewing bearing, the vibration signals are always very weak and overwhelmed by other strong noise, which makes fault feature extraction from the signals very difficult. In order to solve this problem, a denoising method based on multi-scale principal component analysis (MSPCA) and the ensemble empirical mode decomposition (EEMD) is proposed with a new intrinsic mode functions (IMFs) selection strategy. After that, the vibration signal is reconstructed by the selected IMFs. Finally, a method of multi-scale fault frequency extraction of slewing bearing based on EEMD is applied to denoise the vibration signals. The application of this method is demonstrated with laboratory accelerated slewing bearing life test data. Results show that EEMD-MSPCA is more effective in multi-scale fault frequency extraction of low-speed slewing bearing.