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Stochastic resonance in a single-well potential and its application in rolling bearing fault diagnosis
Review of Scientific Instruments ( IF 1.6 ) Pub Date : 2020-06-01 , DOI: 10.1063/1.5143050
Wei Cheng 1 , Xuemei Xu 1 , Yipeng Ding 1 , Kehui Sun 1
Affiliation  

In this paper, a single-well model based on the piecewise function and classical bistable stochastic resonance (CBSR) is proposed. The steady state probability density of particles and mean first passage time in the model are calculated. The output characteristics and performance of the proposed model are analyzed through numerical simulation. On the basis of CBSR and the proposed model, an adaptive system is established (ACSSR) to generate the highest gain of signal-to-noise ratio (SNRg). Finally, the effectiveness of ACSSR in weak signal detection is verified with both simulated and experimental input signals. The results indicate that the ACSSR could detect the defect signal correctly and improve the SNRg.

中文翻译:

单井势随机共振及其在滚动轴承故障诊断中的应用

本文提出了一种基于分段函数和经典双稳态随机共振(CBSR)的单井模型。计算模型中粒子的稳态概率密度和平均首次通过时间。通过数值模拟分析了所提出模型的输出特性和性能。在CBSR和提出的模型的基础上,建立了一个自适应系统(ACSSR)以产生最高的信噪比(SNRg)增益。最后,通过模拟和实验输入信号验证了ACSSR 在弱信号检测中的有效性。结果表明,ACSSR 可以正确检测缺陷信号并提高 SNRg。
更新日期:2020-06-01
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