基于随机共振的轴承早期微弱故障诊断

Bearing Early Weak Fault Diagnosis Based on Stochastic Resonance

针对早期轴承微弱信号难以发现、检测精度低等问题,提出一种基于随机共振的轴承早期微弱故障诊断方法。首先利用排列熵对周期信号的敏感特性,提出一种新的信号筛选方法来对信号进行初步滤波筛选;其次采用随机共振算法对故障信号进行噪声辅助增强,以信噪比为目标函数,采用麻雀搜索算法对随机共振系统参数k、a、b、h进行寻优,最后将寻优后的参数送入随机共振系统进行检测。仿真结果表明:该方法可以有效检测出强噪声中的未知微弱故障信号。

Aiming at the problems of difficult extraction and low detection accuracy of weak signals of bearings at early period, a method for early weak fault diagnosis of bearings was proposed based on stochastic resonance.Based on the sensitivity of permutation entropy criterion to periodic signals, a new signal filtering method was proposed to perform preliminary filtering.Secondly, stochastic resonance algorithm was used for fault signal to noise auxiliary enhancement;taking signal-to-noise ratio as the objective function, the sparrow search algorithm was used to optimize the stochastic resonance system parameters: k,a,b,h.The optimized parameters were sent to the stochastic resonance system for detection.The simulation results show that this method can be used to effectively detect the unknown weak fault signal in strong noise.