基于EEMD和小波包分解在滚动轴承故障信息提取中的分析对比

摘要

由于滚动轴承故障存在很强的调制性，导致检测十分困难，而总体经验模态分解能有效解调信号。所以，将其应用到滚动轴承故障提取中，分析了各个模态中不同的故障成分，并与小波包方法进行对比，说明EEMD在滚动轴承故障检测中更具一定的优势，最后通过采用实验平台的故障轴承数据对其进行了分析，说明EEMD在轴承故障检测中的价值。%It is very difficult for the fault signal of rolling bearing element to extract the fault frequen-cies because the fault signal is modulated and the background noise is very strong. However the rolling bearings’ fault signal is demodulated by using ensemble empirical mode decomposition ( EEMD) , so this method is important for detecting the fault features of rolling bearings. But to contrast wavelet packet de-composition ( WPD) and EEMD, we have proved that the EEMD method is better than WPD method in detecting the fault characteristics of rolling bearings element.