高斯势分段双稳随机共振在不同噪声下的轴承故障诊断

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摘要为解决经典双稳随机共振(Classical Bistable Stochastic Resonance, CBSR)在强噪声下输出信噪比(Signal-to-Noise Ratio, SNR)较低问题，将非饱和分段双稳随机共振(Unsaturated Piecewise Bistable Stochastic Resonance, UPBSR)与高斯势(gaussian potential，GP)随机共振结合得到一种高斯势分段双稳随机共振(Gaussian Potential Piecewise Bistable Stochastic Resonance, GPPBSR)。首先，将GPPBSR与CBSR, UPBSR的势函数分析对比；其次，以SNR和平均信噪比增益(Mean Signal-to-Noise Ratio Increase, MSNRI)为衡量指标，分别在高斯白噪声和色噪声背景下通过遗传算法参数寻优得到SNR和MSNRI随系统参数的变化规律，其结果表明在两种噪声背景下，GPPBSR系统的SNR和MSNRI均优于UPBSR，抗噪性能更好；最后，为验证在不同场景下GPPBSR系统轴承故障诊断的实用性，将UPBSR和GPPBSR系统应用于6205-2RS JEM SKF和HRB 6205-2Z两种型号的轴承故障诊断中，仿真结果表明：GPPBSR系统在两种噪声背景下的故障信号诊断均是可行的，且比CBSR和UPBSR的性能更优越。

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	贺利芳
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关键词 ：轴承故障诊断, 高斯势分段双稳随机共振, 信噪比, 平均信噪比增益, 色噪声

Abstract：To solve the problem of low output Signal-to-Noise Ratio (SNR) of the Classical Bistable Stochastic Resonance (CBSR) in strong noise environment, Unsaturated Piecewise Bistable Stochastic Resonance (UPBSR) and the Gaussian Potential (GP) are combined to obtain a Gaussian Potential Piecewise Bistable Stochastic Resonance (GPPBSR). Firstly, the potential functions of GPPBSR with CBSR and UPBSR are analyzed and compared. Secondly, SNR and Mean Signal-to-Noise Ratio Increase (MSNRI) are used as the measurement index respectively under the background of Gaussian white noise and colored noise. The genetic algorithm is used to optimize the parameters to obtain the characteristics of SNR and MSNRI. The results show that under these two noise backgrounds, UPBSR of SNR and MSNRI are both larger and the noise resistance performance is better. Finally, to verify the usefulness of GPPBSR for diagnosing bearing faults in different scenarios, UPBSR and GPPBSR are applied to the fault diagnosis of the bearings of the 6205-2RS JEM SKF and HRB 6205-2Z models. The simulation results show that GPPBSR can be applied better in these two types of noise environment, and the performance is superior to CBSR and UPBSR.

Key words： Bearing fault diagnosis Gaussian potential segmented bistable stochastic resonance signal-to-noise ratio(SNR) mean signal-to-noise ratio increase( MSNRI) Colored noise;

收稿日期: 2022-01-05 出版日期: 2023-02-15

引用本文:

贺利芳,刘秋玲,张刚. 高斯势分段双稳随机共振在不同噪声下的轴承故障诊断[J]. 振动与冲击, 2023, 42(3): 30-42.
HE Lifang, LIU Qiuling, ZHANG Gang. Bearing fault diagnosis under different noises with GPPBSR system. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(3): 30-42.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I3/30

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