%0 Journal Article
%T 应用EMD和倒包络谱分析的故障提取方法<br>Feature Extraction Method Based on MED and Envelope Cepstrum
%A 孙伟
%A 李新民
%A 金小强
%A 黄建萍
%A 张先辉
%J 振动.测试与诊断
%D 2018
%R 10.16450/j.cnki.issn.1004-6801.2018.05.028
%X 针对倒频谱分析方法难以提取滚动轴承早期微弱故障的问题，提出了一种利用最小熵反褶积(minimum entropy deconvolution，简称MED)和倒包络谱分析的故障特征提取方法，并应用于滚动轴承诊断中。首先，采用MED方法对故障信号进行降噪处理，同时增强信号中的周期成分；然后，计算降噪后信号的包络，再对包络信号进行倒频谱分析；最后，得到倒包络谱，提取故障特征。试验结果表明，所提出的方法优于传统的倒频谱分析，能够有效提取强背景噪声下的滚动轴承早期故障特征频率信息。<br>In the light of the difficulty for the cepstrum method to draw early weak fault, a new feature extraction method based on minimum entropy deconvolution (MED) and envelope cepstrum is proposed. First, the fault signal is de-noised and the meantime periodic impact components are enhanced by MED method to get the envelope signal. Then, the envelope cepstrum is presented by the cepstrum analysis of the envelope signal. Finally, the fault features are extracted through the envelope cepstrum. The results of the experiments show that the proposed method extract the feature frequency information of incipient fault with higher efficiency than the traditional methods do and can be used to prevent major faults from occurring．
%K 滚动轴承
%K 故障诊断
%K 倒频谱
%K 最小熵反褶积
%K 包络信号&lt
%K br&gt
%K rolling bearing
%K fault diagnosis
%K cepstrum
%K minimum entropy deconvolution
%K envelope signal
%U http://zdcs.nuaa.edu.cn/ch/reader/view_abstract.aspx?file_no=201805028&flag=1