• CN:11-2187/TH
  • ISSN:0577-6686

›› 2011, Vol. 47 ›› Issue (21): 58-63.

• 论文 • 上一篇    下一篇

采用粒子群算法的冲击信号自适应单稳态随机共振检测方法

李继猛;陈雪峰;何正嘉   

  1. 西安交通大学机械制造系统工程国家重点实验室
  • 发布日期:2011-11-05

Adaptive Monostable Stochastic Resonance Based on PSO with Application in Impact Signal Detection

LI Jimeng;CHEN Xuefeng;HE Zhengjia   

  1. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University
  • Published:2011-11-05

摘要: 针对冲击信号阱内共振的特点、随机共振系统参数合理选取缺乏有效的理论依据以及传统自适应随机共振单参数优化的不足,提出一种基于粒子群算法(Particle swarm optimization, PSO)的多参数同步优化自适应单稳态随机共振方法。该方法采用单稳态随机共振模型,避免了经典双稳系统势阱间的共振跃迁,并选用单稳态系统输出的加权峭度指标作为PSO的适应度函数,能够实现多参数的同步自适应选取,从而最优地检测出原始信号中的冲击成分。将PSO和变尺度随机共振相结合,并给出冲击信号自适应随机共振算法流程。该算法被用于仿真冲击信号与工程实际中冲击信号的检测,结果表明所提方法简单易行,收敛速度快,能够有效提高信噪比,具有良好的工程应用前景。

关键词: 冲击信号, 加权峭度指标, 粒子群算法, 随机共振, 自适应

Abstract: Based on the characteristics of the resonance in single potential well of impact signal and the reasonable selection of parameters lacking of effective theoretical basis and the deficiency of single parameter optimization in traditional adaptive stochastic resonance, a new adaptive monostable stochastic resonance based on particle swarm optimization(PSO), which can realize multi-parameter synchronous optimization, is proposed. The model of monostable stochastic resonance that avoids the resonance transition between potential wells in classical bistable system is applied and the weighted kurtosis index of the output of the monostable system is determined as the fitness function of PSO algorithm and multi-parameter in stochastic resonance are selected adaptively. As a result, the impact components in original signals are detected effectively. The algorithm procedure of the impact with adaptive stochastic resonance, combining PSO with the scale transformation stochastic resonance, is provided. The proposed method is applied to the simulation of impact signal and the detection of engineering impact signal, and the results show that the presented method can raise the signal-to-noise ratio effectively, which has advantages of simplicity and fast convergence speed, and possesses a good prospect of engineering application.

Key words: Adaptive, Impact signal, Monostable stochastic resonance, Particle swarm optimization, Weighted kurtosis index

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