基于改进多稳态系统随机共振的轴承微弱故障诊断

Weak Bearing Fault Diagnosis Based on Improved Stochastic Resonance of the Multi-Stable System

针对传统随机共振方法在强噪声背景下对轴承微弱故障诊断中存在严重的边频干扰问题,提出了一种应用改进多稳态随机共振模型进行轴承微弱故障诊断的方法.在高斯白噪声和周期性激励作用下,推导得到了改进多稳态系统的平均首次穿越时间和功率谱放大因子的解析表达式.研究发现,存在一组最优的参数使得改进多稳态系统的随机共振效应最大化.将改进的多稳态随机共振模型应用于轴承内外圈的微弱故障诊断,并利用量子粒子群优化算法对系统参数和阻尼系数进行优化.研究结果表明,所提方法能够在强噪声背景下有效识别出微弱故障特征频率,且与传统多稳态随机共振方法相比,该方法解决了严重的边频干扰问题,输出信号特征频率处的频谱峰值更高,大大提高了轴承微弱故障诊断的性能.

To deal with the serious problem of side frequency interference in the traditional stochastic resonance method,an improved multi-stable stochastic resonance model was proposed for the weak bearing fault diagnosis in a strong noise background.In the presence of Gaussian white noise and a periodic force,the analytical expressions of mean first passage time and spectral amplification were obtained.Results revealed that there was an optimal set of system parameters to maximize the stochastic resonance effect of the improved multi-stable model.This improved multi-stable stochastic resonance model was applied to the weak fault diagnosis of inner and outer rings of bearings.Additionally,the quantum particle swarm optimization algorithm was employed to optimize the system parameters and the damping coefficient.It is shown that the proposed method could effectively identify the weak fault characteristic frequencies in a strong noise background.Compared with the traditional multistable stochastic resonance method,this method solves the problem of serious side frequency interference and raises the spectrum peak at the characteristic frequency of the output signal,greatly improving the performance of weak bearing fault diagnosis.