EMD-ICA联合降噪在滚动轴承故障诊断中的应用  

Application of EMD-ICA to Fault Diagnosis of Rolling Bearings

作　　者：张俊红[1] 李林洁[1] 马文朋[1] 李周裕[1] 刘昱[1] 

机构地区：[1]天津大学内燃机燃烧学国家重点实验室,天津300072

出　　处：《中国机械工程》2013年第11期1468-1472,共5页China Mechanical Engineering

基　　金：国家自然科学基金委员会与中国民用航天局联合资助项目(U1233201)

摘　　要：滚动轴承早期故障信号具有能量小、频带分布宽等特征,易受到其他能量较大的振源信号的干扰,致使传统滤波降噪方法存在很大的局限性。针对这一特点,提出经验模式分解(EMD)和独立分量分析(ICA)相结合的联合降噪新方法。将单通道振动信号进行EMD分解,基于互相关准则对分解后的本征模函数进行重组,构造虚拟噪声通道,并以此作为ICA的输入矩阵,采用FastICA算法实现源信号和噪声信号的分离,从而达到降噪的目的。将该方法应用于滚动轴承故障诊断中,对降噪后的重构信号进行频谱分析,进而判断滚动轴承的运行状态。仿真和试验分析结果表明该方法有效可行。Vibration signals generated by incipient faults of rolling element bearing was usually with low energy and dispersed frequency distribution, they were easily merged in strong disturbances of other vibration sources. According to this feature of rolling beating＇s vibration signals, a new fault -diagnosis approach based on the combination of EMD and /CA was proposed. With the approach, single channel vibration signals were decomposed using EMD method and modal components were ob- tained at first, then they were restructured based on mutual correlation criterion. Therefore multi- dimensional virtual noise channels were constructed, source signals and noise signals were separated when FastICA method was adopted with multi-dimensional virtual noise channels used as input ma- trix of ICA. The method was applied to fault diagnosis of rolling bearings. The denoised signals were analyzed by fast Fourier transform method, then the results were compared with the fault characteris- tics to judge operation state of the rolling element bearings. Simulations and tests verify the feasibility of the method.

关 键 词：经验模式分解 独立分量分析 滚动轴承 故障诊断 

分 类 号：TH212[机械工程—机械制造及自动化] TH213.3