采用遗传算法的自适应随机共振系统弱信号检测方法研究  

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

作　　者：王晶[1,2] 张庆[1,2] 梁霖[1,2] 张熠卓[1] 徐光华[1,2] 

机构地区：[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

出　　处：《西安交通大学学报》2010年第3期32-36,共5页Journal of Xi'an Jiaotong University

基　　金：国家高技术研究发展计划资助项目(2007AA04Z432);机械制造系统工程国家重点实验室开放课题研究基金资助项目

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.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：随机共振 遗传算法 多参数同步优化 弱信号检测 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置]