基于EEMD与空域相关降噪的滚动轴承故障诊断方法  

Fault diagnosis for rolling bearing based on EEMD and spatial correlation denoising

作　　者：田晶[1] 王英杰 王志[1] 艾延廷[1] 孙丹[1] Tian Jing;Wang Yingjie;Wang Zhi;Ai Yanting;Sun Dan(Liaoning Key Laboratory of Advanced Test Technology for Aeronautical Propulsion System,Shenyang Aerospace University,Shenyang 110136,China)

机构地区：[1]沈阳航空航天大学辽宁省航空推进系统先进测试技术重点实验室

出　　处：《仪器仪表学报》2018年第7期144-151,共8页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(11702177);辽宁省教育厅项目(LN201710)资助

摘　　要：针对滚动轴承早期故障信号非平稳、非线性,强噪声的特点,提出了一种将集成经验模态分解（EEMD）和空域相关降噪相结合的滚动轴承故障诊断方法。该方法首先采用EEMD对滚动轴承故障信号进行分解,得到若干个IMF分量,其次,采用峭度—度量因子准则筛选出有效的IMF分量进行信号重构,然后,采用空域相关降噪方法对重构信号进行降噪处理,最后,提取降噪后信号的故障特征频率对轴承故障进行诊断。采用建立的方法对某轴承内圈、外圈故障实验数据进行了分析与诊断,结果表明,方法能够有效克服信号分解的模态混叠效应,对故障信号噪声抑制效果明显,并能准确有效地提取出轴承的故障特征频率,实现对滚动轴承故障的精确诊断。To solve the problems of non-stationary,nonlinear and strong noise of rolling bearing early fault signal,this paper presents a rolling bearing fault diagnosis method based on the combination of the ensemble empirical mode decomposition（ EEMD） and the spatial correlation denoising. Firstly,the rolling bearing fault signals are decomposed into several IMF components by EEMD. Secondly,the kurtosis-metric factor criterion is utilized to filter out the effective IMF components for signal reconstruction. Then,the reconstructed signal is denoised through the spatial correlation noise reduction method. Finally,the fault characteristic frequency of the denoised signal is extracted to diagnose the bearing fault. Experimental data of the inner and outer race in the bearing are analyzed and diagnosed by the proposed method. The results show that the method can effectively overcome the mode-aliasing effect of signal decomposition and significantly suppress the noise of the fault signal. This method can also accurately and effectively extract the characteristic frequency of bearing fault to diagnose the rolling bearing fault accurately.

关 键 词：集成经验模态分解 空域相关降噪 滚动轴承 故障诊断 度量因子 

分 类 号：V231.92[航空宇航科学与技术—航空宇航推进理论与工程] TH133.33[机械工程—机械制造及自动化]