改进DLMD和TKEO的滚动轴承故障特征提取方法

Improved DLMD and TKEO Method for Fault Feature Extraction of Rolling Bearing

针对微分局部均值分解(Differential Local Mean Decomposition,DLMD)不能自适应判断微分次数的问题,提出一种改进DLMD和Teager能量算子(Teager-Kaiser Energy Operator,TKEO)解调的滚动轴承故障特征提取方法.首先,构建中点-局部均值距离与绝对偏度之和的DLMD微分次数判定指标,将信号分解为若干个乘积函数(Product Function,PF)分量;其次,计算敏感因子筛选有效PF分量并重构;最后,计算TKEO谱,提取滚动轴承的故障特征.实验对比分析表明,所提方法能自适应判断DLMD的微分次数,并有效提取滚动轴承故障特征.

In order to solve the problem that differential local mean decomposition(DLMD)can’t adaptively determine the differential degree,a rolling bearing fault feature extraction method based on improved DLMD and Teager-Kaiser energy operator(TKEO)demodulation is proposed.Firstly,the index of DLMD differential degree based on the sum of midpoint local mean distance and absolute skewness is constructed,and the signal is decomposed into several product function(PF)components;Secondly,the sensitive factors are calculated,and the effective PF components were screened and reconstructed;Finally,the TKEO spectrum is calculated to extract the fault features of the rolling bearing.The experimental results show that the proposed method can adaptively judge the differential degree of DLMD and effectively extract the fault features of rolling bearing.