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  

Analysis of extracting the fault information about rolling bearings based on EEMD and WPD

作　　者：寇为刚[1] 谭等泰[1] 

机构地区：[1]甘肃政法学院公安技术学院,兰州730070

出　　处：《工业仪表与自动化装置》2015年第4期101-104,共4页Industrial Instrumentation & Automation

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 frequencies 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 decomposition（ WPD） and EEMD,we have proved that the EEMD method is better than WPD method in detecting the fault characteristics of rolling bearings element.

关 键 词：滚动轴承 经验模态分解 小波包分解 特征提取 

分 类 号：TP206.3[自动化与计算机技术—检测技术与自动化装置]