feature extraction for rolling element bearing weak fault based on momeda and iceemdan

There are always the nonlinear and non-stationary characteristics and periodic pulse in vibration signals of rolling element bearings when there are partial faults in those bearings. Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) overcomes the presence of spurious modes and residual noise in Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), but it cannot clearly and accurately extract the weak fault feature of rolling element bearings under the strong background noise. Here, Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) was proposed. A bearing simulator was used to collect vibration signals of bearing inner and outer race, which was enhanced by MOMEDA, decomposed into several Intrinsic Mode Functions(IMFs) by ICEEMDAN, and analyzed by the envelope demodulation, finally gaining the frequency of shaft speed, BPFI (ball pass frequency, inner race) and harmonics, sidebands spaced, BPFO (ball pass frequency, outer race) and harmonics. The results show that this method can be used to accurately extract different frequency components of bearing fault vibration signals and diagnose bearing different fault location.