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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摘要轴承作为旋转机械的重要组件之一，及时对其进行健康监测与更换可有效避免设备停机，减少经济损失。本文首先基于自构建关联噪声驱动下的随机共振系统（Stochastic Resonance System Driven by Self-constructingly Correlated Noise, DSCSR），推导了在正弦激励下该系统输出的理论信噪比（signal-to-noise ratio, SNR）。研究发现通过调节此非线性系统的参数可观察到随机共振现象。其次，针对将随机共振现象用于故障诊断时需要准确的先验知识这一局限性，本文进一步提出了基于功率谱的信噪比评价指标，并以此来确定非线性系统随机共振发生时的最优系统参数，对最优参数系统输出信号进行功率谱分析来判断故障类型。最后，通过轴承故障诊断实验以及实际风机轴承内圈故障实例证明了DSCSR方法的有效性，以及其增强微弱故障特征并抑制其他谐波以及噪声的干扰的能力。

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	徐海涛 1
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关键词 ：关联噪声, 随机共振, 故障诊断, 非线性系统

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

收稿日期: 2023-04-10 出版日期: 2024-06-15

引用本文:

徐海涛 1,2，杨涛 1,2，周生喜 1,2. 自构建关联噪声下的随机共振及其在故障诊断上的应用[J]. 振动与冲击, 2024, 43(11): 297-305.
XU Haitao1,2, YANG Tao1,2, ZHOU Shengxi1,2. Stochastic resonance driven by self-constructingly correlated noise and its application in fault diagnosis. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(11): 297-305.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2024/V43/I11/297

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