基于CYCBD和包络谱的滚动轴承微弱故障特征提取  

Feature Extraction of Weak Fault for Rolling Bearing based on CYCBD and Envelope Spectrum

作　　者：赵晓涛 孙虎儿[1] 姚巍 Zhao Xiaotao;Sun Huer;Yao Wei(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》2020年第4期165-169,176,共6页Journal of Mechanical Transmission

基　　金：山西省自然科学基金(201801D121186)。

摘　　要：针对在强噪声的干扰下,滚动轴承微弱故障特征难以有效地提取的问题,提出一种基于最大2阶循环平稳盲解卷积(Maximum Second-order Cyclostationarity Blind Deconvolution,CYCBD)和包络谱相结合的微弱故障特征提取方法。首先,由故障特征频率设置合理的循环频率集,使用CY-CBD对含有强噪声的微弱故障冲击信号进行降噪处理,增强信号中的周期性冲击成分;然后,对降噪信号进行Hilbert包络谱分析来识别故障特征频率。通过仿真和实验,结果证明,该方法能有效地提取被强噪声淹没的微弱故障特征。To solve the problem that it is difficult to extract the weak fault features of rolling bearing effectively under the interference of strong background noise,a method of extracting the weak fault features based on the combination of maximum second-order cyclostationary blind deconvolution(CYCBD) and envelope spectrum is proposed.Firstly,a reasonable cycle frequency set is set by the fault characteristic frequency,and CYCBD is used to reduce the noise of weak fault impulse signal with strong noise,so as to enhance the periodic impulse component in the signal.Then,the noise reduction signal is analyzed by Hilbert envelope spectrum to identify the fault characteristic frequency.The simulation and experimental results show that the method can effectively extract the weak fault features submerged by strong noise.

关 键 词：滚动轴承 最大2阶循环平稳盲解卷积 微弱故障 特征提取 

分 类 号：TN911.7[电子电信—通信与信息系统] TH133.33[电子电信—信息与通信工程]