Adaptive Stochastic Resonance Based on Genetic Algorithm with Applications in Weak Signal Detection

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Adaptive Stochastic Resonance Based on Genetic Algorithm with Applications in Weak Signal Detection

更新时间：2025-05-01

- Adaptive Stochastic Resonance Based on Genetic Algorithm with Applications in Weak Signal Detection
- Vol. 44, Issue 3, Pages: 32-36(2010)
- 作者机构：
  
  1. 西安交通大学机械工程学院,西安,710049
  2. 西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP206
- Online First：10 March 2010，
  
  Published：2010
- 稿件说明：
移动端阅览
Adaptive Stochastic Resonance Based on Genetic Algorithm with Applications in Weak Signal Detection[J]. 2010, 44(3): 32-36.
DOI：

Adaptive Stochastic Resonance Based on Genetic Algorithm with Applications in Weak Signal Detection[J]. 2010, 44(3): 32-36. DOI：

摘要

针对传统自适应随机共振系统只能实现单参数优化的缺点

提出了一种基于遗传算法的多参数同步优化自适应随机共振算法.该算法选用由双稳系统输出的信噪比作为遗传算法的适应度函数

能够实现随机共振系统中多个参数的自适应选取

从而最优地检测出原始信号中的微弱周期成分.同时

将该优化算法和移频变尺度随机共振相结合

可以实现大参数条件下的随机共振.仿真数据和滚动轴承外环故障数据的分析表明

该算法收敛速度快

简单易行

在采样点数较少的条件下能从强噪声背景中检测出微弱的高频周期成分

因此具有良好的工程应用前景.

Abstract

One deficiency of the traditional adaptive stochastic resonance is that only a single parameter can be optimized while the other parameters in the system being fixed. A new adaptive stochastic resonance based on genetic algorithm

which realizes multi-parameter synchronous optimization

is proposed. The signal-noise-ratio of the output of the bi-stable system is determined as the fitness function of genetic algorithm and multi-parameters in stochastic resonance system are selected adaptively. As a result

weak periodical components in original signals are sufficiently amplified. Simultaneously

the optimization algorithm

combined with frequency-shifted and re-scaling stochastic resonance

enables to achieve the stochastic resonance under the conditions of great parameters. The proposed method is evaluated by simulation data and vibration signals measured on defective bearings with outer race fault. The results show that weak periodical components with high frequency buried in strong noise are well extracted in case of small number of sample points.

关键词

Keywords

references

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