基于粒子群优化的CYCBD在滚动轴承故障特征提取的应用研究  

Study on Application of CYCBD based on PSO in Fault Feature Extraction of Rolling Bearing

作　　者：刘宇涛 孙虎儿[1] Liu Yutao;Sun Huer(School of Mechanical Engineering,North University of China,Taiyuan 030051,China)

机构地区：[1]中北大学机械工程学院,山西太原030051

出　　处：《机械传动》2021年第2期171-176,共6页Journal of Mechanical Transmission

基　　金：山西省自然科学基金(201801D121186)。

摘　　要：针对在背景噪声下滚动轴承故障初期周期性瞬态冲击不明显的问题,应用基于循环平稳最大化盲解卷积方法(Blind deconvolution based on cyclostationarity maximization,CYCBD)。滤波器长度和循环频率左右CYCBD降噪效果,应用粒子群优化算法(Particle swarm optimization,PSO)对其进行智能化寻优,确定优化参数,解决CYCBD不稳定问题。首先,采用PSO优化CYCBD中滤波器长度和循环周期频率,对周期性冲击成分进行增强;然后,通过包络谱峰值因子(Crest factor of envelope spectrum,EC)作为PSO的目标函数,迭代寻找滤波器长度和循环周期频率的最优解;最后,对CYCBD应用最优解,对增强后的信号进行包络解调分析,可以准确地获得轴承信号的故障特征频率。通过对仿真信号和实验数据分析,表明该方法可有效增强振动信号的周期性瞬态冲击特征,在滚动轴承早期故障特征提取方面具有优势。The blind deconvolution based on cyclostationarity maximization(CYCBD)is applied to solve the problem that the periodic transient impacts is not obvious at the initial stage of rolling bearing failure under background noise.The noise reduction effect of CYCBD around the filter length and cycle frequency,particle swarm optimization algorithm(PSO)is applied to intelligently optimize CYCBD.Determine the optimal parameters to solve the instability of CYCBD.Firstly,PSO is used to optimize the filter length and cycle frequency in CYCBD to enhance the periodic impacts component.Then,the optimal solution of filter length and cycle frequency is iteratively found by using crest factor of envelope spectrum(EC)as the objective function of PSO.Fi--nally,by applying the optimal solution to CYCBD and conducting envelope demodulation analysis on the enhanced signals,the fault characteristic frequency of bearing signals can be accurately obtained.Through the analysis of simulation signals and experimental data,it is shown that the method can effectively enhance the periodic transient impacts characteristics of vibration signals and has advantages in the early fault feature extraction of rolling bearings.

关 键 词：滚动轴承 循环平稳最大化的盲反褶积 粒子群优化算法 滤波器长度 循环频率 

分 类 号：TH133.33[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]