基于噪声辅助信号特征增强的滚动轴承早期故障诊断

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Abstract An early fault diagnosis method for rolling bearing based on the feature enhancement of noise-assisted signal was proposed, aiming at the fault feature of rolling bearing vibration signal, some useful signals are filtered out while filtering out the interference noise, which causes the loss of characteristic signal. The generalized multi-scale permutation entropy screening criterion was used to screen the vibration signal, and the parameters of the Duffing vibration subsystem were optimized by the particle swarm optimization algorithm to achieve the optimal matching among the Duffing vibration subsystem and the input signal and noise, thereby improving the stochastic resonance effect. Part of the background noise energy is transferred to the early weak fault signal feature of the rolling bearing, which enhances the feature of the early weak fault signal. The proposed method was applied to the early fault diagnosis of rolling bearing life state, and compared with the adaptive morphology method based on variational mode decomposition. The results show the effectiveness and feasibility of the proposed method.

Key words： rolling bearing Duffing vibrator generalized multiscale permutation entropy stochastic resonance

Received: 26 February 2019 Published: 28 July 2020

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	Articles by authors
	ZHOU Yiwen1
	CHEN Jinhai1
	WANG Heng1
	JIANG Jie2

Cite this article:

ZHOU Yiwen1,CHEN Jinhai1,WANG Heng1, et al. Early fault diagnosis for rolling bearing based on noise-assisted signal feature enhancement[J]. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(15): 66-73.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2020/V39/I15/66

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