Nonlinear coupled asymmetric stochastic resonance for weak signal detection based on intelligent algorithm optimization

IF 3 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics Pub Date : 2024-10-01 DOI:10.1016/j.probengmech.2024.103697

Shaojuan Ma , Yuan Liu , Xiaoyan Ma , Yantong Liu

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引用次数: 0

Abstract

Stochastic resonance has been extensively studied for detecting weak signals. To improve the diagnostic ability of weak signals, a novel nonlinear coupled asymmetric stochastic resonance (NCASR) system is investigated in this paper. Firstly, the NCASR system is established by coupling the asymmetric bistable system with the monostable system. Next, the expressions for the steady-state probability density (SPD) function, the mean first passage time (MFPT) and the signal-to-noise ratio (SNR) of the proposed system are derived based on the adiabatic approximation theory. Furthermore, the impact of system parameters on the SPD, the MFPT and the SNR is analyzed. Then, by simulation experiments, we verify the effectiveness of detecting weak signals for the NCASR system with Lévy noise. Finally, the NCASR system optimized by Adaptive Weighted Particle Swarm Optimization (AWPSO) algorithm is applied to detect the bearing fault signal. Compared with the optimized classical bistable stochastic resonance (CBSR) system, it is found that the detection performance of the NCASR system is superior to the CBSR system in detecting bearing fault signals.

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基于智能算法优化的用于微弱信号检测的非线性耦合非对称随机共振

随机共振在探测微弱信号方面得到了广泛的研究。为了提高对微弱信号的诊断能力，本文研究了一种新型非线性耦合非对称随机共振（NCASR）系统。首先，通过将非对称双稳态系统与单稳态系统耦合，建立了 NCASR 系统。接着，根据绝热近似理论推导出了所提系统的稳态概率密度（SPD）函数、平均首次通过时间（MFPT）和信噪比（SNR）的表达式。此外，还分析了系统参数对 SPD、MFPT 和信噪比的影响。然后，通过模拟实验，我们验证了具有 Lévy 噪声的 NCASR 系统检测微弱信号的有效性。最后，应用自适应加权粒子群优化算法（AWPSO）优化的 NCASR 系统检测轴承故障信号。与优化后的经典双稳态随机共振（CBSR）系统相比，发现 NCASR 系统在检测轴承故障信号方面的性能优于 CBSR 系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Probabilistic Engineering Mechanics 工程技术-工程：机械

CiteScore

3.80

自引率

15.40%

发文量

审稿时长

13.5 months

期刊介绍： This journal provides a forum for scholarly work dealing primarily with probabilistic and statistical approaches to contemporary solid/structural and fluid mechanics problems encountered in diverse technical disciplines such as aerospace, civil, marine, mechanical, and nuclear engineering. The journal aims to maintain a healthy balance between general solution techniques and problem-specific results, encouraging a fruitful exchange of ideas among disparate engineering specialities.