A method of fault diagnosis of rolling bearings based on ACMD and improved MOMEDA

As it is difficult to extract features of rolling bearings under strong background noise, a rolling bearing fault diagnosis method based on the adaptive chirp mode decomposition (ACMD) and the improved multipoint optimal minimum entropy deconvolution adjusted (IMOMEDA) was proposed. Firstly, the ACMD was integrated with a Gini index-based regrouping scheme to improve the signal-to-noise ratio. Secondly, an improved MOMEDA was proposed. In the method, the multipoint kurtosis value was used as an objective function, applying the aquila optimizer to get the optimal period parameter of MOMEDA self-adaptively for the accuracy of parameter setting. Finally, signal envelope spectrum analysis was used to determine the fault location. Simulation and analysis results of the measured data show that the proposed method can effectively extract the features of the rolling bearing fault signals under strong background noise, and has certain superiority and practicality. © 2023 Chinese Vibration Engineering Society. All rights reserved.