自构建关联噪声下的随机共振及其在故障诊断上的应用

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Abstract Rolling element bearings are the crucial component of rotating machine, timely health monitoring can effectively avoid the breakdown of the machine, further reduce the loss of the economic. Firstly, this paper proposed a stochastic resonance system driven by self-constructingly correlated noise (DSCSR), and theoretically analyzed the signal-to-noise ratio (SNR), which examines that the stochastic resonance can occur by adjusting the systems parameters. Secondly, aiming at the drawback that the accurate prior knowledge should be obtained before analysis, the paper suggested to calculate the SNR based on the power spectrum (〖SNR〗_P). Based on 〖SNR〗_P, the optimized system parameters can be achieved. Therefore, the fault type can be determined according to the output signal of the optimized system. Finally, the bearing fault diagnosis experiment and the bearing inner race fault of an actual wind turbine validate the capability of DSCSR in enhancing the weak fault characteristic and in suppressing the interference of other harmonics or random noise.

Key words： Correlated noise stochastic resonance fault diagnosis nonlinear system

Received: 10 April 2023 Published: 15 June 2024

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	Articles by authors
	XU Haitao1
	2
	YANG Tao1
	2
	ZHOU Shengxi1
	2

Cite this article:

XU Haitao1,2,YANG Tao1, et al. Stochastic resonance driven by self-constructingly correlated noise and its application in fault diagnosis[J]. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(11): 297-305.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2024/V43/I11/297

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