基于ACMD与改进MOMEDA的滚动轴承故障诊断

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Abstract 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 (MK) value was used as an objective function, applying the Aquila Optimizer (AO) 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. The 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.

Key words： Adaptive Chirp Mode Decomposition Gini Index Aquila Optimizer Multipoint Optimal Minimum Entropy Deconvolution Adjusted rolling bearing fault diagnosis

Received: 22 September 2022 Published: 28 August 2023

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	SHI Jia
	HUANG Yufeng
	WANG Feng

Cite this article:

SHI Jia,HUANG Yufeng,WANG Feng. A method of fault diagnosis of rolling bearings based on ACMD and improved MOMEDA[J]. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(16): 218-226.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2023/V42/I16/218

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