多通道相关-经验模式分解在滚动轴承故障诊断中的应用

摘要

在处理非平稳振动信号时,经验模式分解(EMD)的应用较为广泛.针对滚动轴承的早期故障信号中含有强烈的背景噪声,诊断效果有时也不够明显的情况,本文提出了多通道相关-经验模式分解方法.首先通过EMD将滚动轴承故障信号分解成若干本征模态函数(IMF)分量;然后对IMF分量进行多相关处理,取相关性最强的IMF分量进行自适应重构;最后通过循环谱分析识别出滚动轴承的故障类型.将该方法应用到滚动轴承的仿真故障数据和实际数据中,分析结果表明,该方法可以更加有效地提取滚动轴承故障特征频率信息,突出故障频率.%Experience mode decomposition (EMD) is most widely used in processing non-stationary vibration signals.In order to solve the problems that some diagnosis effects achieved by this method are not obvious enough sometimes due to the strong background noise involved in the early fault signal of rolling bearings,the Multi-channel correlation empirical mode decomposition (MCC-EMD) based on EMD is proposed.Firstly,EMD is used to decompose the fault signal into several intrinsic mode functions (IMFs).Secondly,the Multi-correlation process is made for the IMFs and adaptive reconstruction is performed by the strongest correlation of IMFs.Finally,the fault type of rolling bearing is identified by cyclic spectrum analysis.The proposed method is applied to simulated signals and actual signals,and the results show that the method can effectively extract the weak feature frequency information of incipient fault of rolling bearing.