基于二阶非对称双稳方法的弱信号检测及应用  

Weak signal detection and application based on second‑order asymmetric bistable method

作　　者：李思雨 张建刚[1] LI Siyu;ZHANG Jiangang(College of Mathematics and Physics,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学数理学院,甘肃兰州730070

出　　处：《武汉大学学报（工学版）》2024年第8期1176-1190,共15页Engineering Journal of Wuhan University

基　　金：国家自然科学基金项目(编号:61863002)。

摘　　要：针对一种非对称欠阻尼连续双稳态随机共振系统,建立了一种新的势函数模型。首先,在绝热条件下推导了该系统的平均首次通过时间(mean first pass time,MFPT)以及信噪比(signal-to-noise ratio,SNR)等表达式;其次,分别研究了系统参数、非对称系数、阻尼因子对SNR的影响;此外,在数字信号处理应用中,采用4阶Runge-Kutta算法使该2阶随机共振模型能够检测大参数信号;最后,基于随机权重粒子群(random weighted particle swarm optimization,RWPSO)算法选择最优输出,将SNR作为测试系统的性能评价指标。仿真模拟与实验结果表明,与传统系统相比,新势函数模型的系统具有更大的辨识度,该系统可以获得较优的输出SNR和更高的故障信号特征频率。Aiming at an asymmetric underdamped continuous bistable stochastic resonance system,a new potential function model was established.Firstly,under adiabatic conditions,the expressions of mean first pass time(MFPT)and signal-to-noise ratio(SNR)of the system were derived.Secondly,the effects of system parameters,asymmetric coefficient and damping factor on SNR were studied respectively.In addition,in applications of digital signal processing,the fourth-order Runge-Kutta algorithm was used to enable the secondorder stochastic resonance model to detect large-parameter signals.Finally,the optimal output was selected based on random weighted particle swarm optimization(RWPSO)algorithm,and the SNR was used as the performance evaluation index of the test system.The simulation and experimental results show that compared with the traditional system,the system with the new potential function model has a greater degree of identification,and the system can obtain a better output SNR and a higher characteristic frequency of fault signals.

关 键 词：随机共振 二阶分段非对称双稳态 随机权重粒子群算法 微弱信号 

分 类 号：O193[理学—数学] O29[理学—基础数学]