基于自适应蝙蝠算法随机共振的轴承故障诊断

Bearing fault diagnosis of stochastic resonance based on adaptive bat algorithm

针对传统的随机共振系统无法自适应调节结构参数的问题,以及采用自适应遗传算法和群智能算法的随机共振系统随着种群数量的增加算法容易造成局部寻优的缺陷,提出了一种基于自适应蝙蝠算法的随机共振滚动轴承故障诊断方法。该方法基于双稳随机共振系统模型,结合二次采样频率变换思想,通过自适应蝙蝠算法优化双稳随机共振的结构参数,从而使故障信号得到增强,实现滚动轴承的故障诊断。为了验证该方法的可行性和优越性,进行了仿真数据分析和轴承内外圈故障的应用诊断。结果表明,该方法模型简单,算法参数少,收敛速度快,能够准确高效地实现轴承内外圈故障的诊断。

Aiming at the problem that the traditional stochastic resonance system cannot adjust the structural parameters adaptively,and that stochastic resonance system using the adaptive genetic algorithm and swarm intelligence algorithm is easy to cause the defect of local optimization with the increase of the population number,a fault diagnosis method of stochastic resonance based on adaptive bat algorithm is proposed.The method is based on the model of bistable stochastic resonance system,combined with the idea of twice sampling frequency transformation,and output the structural parameters of bistable stochastic resonance by adaptive bat algorithm,so as to enhance the effect of fault signal and realize the fault diagnosis of rolling bearing.In order to verify the feasibility and superiority of this method,the simulation data analysis and the application diagnosis of bearing inner and outer ring fault are carried out.The results show that the model of the method is simple,the parameters of the algorithm are few,the convergence speed is fast,and it can accurately and efficiently realize the fault diagnosis of bearing inner and outer rings.