Research about rolling element bearing fault diagnosis based on mathematical morphology and sample entropy

被引：0
|
作者：
Cui, Lingli [1 ]
Gong, Xiangyang [1 ]
Zhang, Yu [1 ]
机构：
[1] Beijing Univ Technol, Beijing Engn Res Ctr Precis Measurement Technol &, Beijing, Peoples R China
来源：
PROCEEDINGS OF THE 2015 4TH INTERNATIONAL CONFERENCE ON SENSORS, MEASUREMENT AND INTELLIGENT MATERIALS | 2016年 / 43卷
关键词：
mathematical morphology; pattern spectrum; sample entropy; BP neural network;
D O I：
暂无
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
In view of the non-linear and non-stationary of the rolling element bearing fault signal, the method of mathematical morphology analysis is introduced into the rolling element bearing fault diagnosis. Multi-scale morphological transform is applied to the analysis of the bearing signals. To describe the complexity of pattern spectrum curves by using sample entropy, and its value as the input vector of the neural network is used to realize the fault pattern classification by using the back-propagation (BP) neural network. Experimental results show that this method is effective.
引用
收藏
页码：126 / 129
页数：4
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