Abstract: Fault signal of rolling bearing was easily submerged in strong background noise during actual operation, which made it difficult to identify the fault type. Aiming at these problems, a joint noise reduction method based on intrinsic time-scale decomposition (ITD) and multipoint optimal minimum entropy deconvolution adjusted(MOMEDA) was proposed, and applied to the fault diagnosis of rolling bearing. Firstly, the ITD algorithm was used to decompose the original signal of the rolling bearing fault to obtain multiple proper rotation components (PRC); Secondly, according to the principle of correlation coefficient and kurtosis, the PRC that had a greater correlation with the original signal was selected for reconstruction; Then, MOMEDA algorithm was used to further denoise the reconstructed signal to separate the useful signal from the noise signal. Finally, the envelope demodulation analysis of the signal was performed to extract the fault characteristic frequency and diagnose the specific location of the bearing fault. In addition, in order to verify the effectiveness of the method, the simulation signals were compared and analyzed by ITD and local mean value decomposition (LMD), MOMEDA and maximum correlation kurtosis deconvolution (MCKD), and the analysis of the outer ring instance was presented. The results indicate that the diagnosis acuracy of the joint noise reduction method based on ITD-MOMEDA is 4.3% higher than the ITD-MCKD diagnosis accuracy, which can more effectively remove strong noise and successfully detect the type of bearing failure.

Key words:  rolling bearing; bearing failure; intrinsic time-scale decomposition (ITD); multipoint optimal minimum entropy deconvolution adjusted(MOMEDA); proper rotation components (PRC); envelope demodulation analysis

ZHU Zi-yue, ZHANG Jin-ping. Fault diagnosis of rolling bearing based on ITD-MOMEDA combined noise reduction［J］.Journal of Mechanical & Electrical Engineering, 2022,39(2):217-223.