Bearing Fault Recognition Based on Feature Extraction and Clustering Analysis

被引：0
|
作者：
Zhang, Xin [1 ]
Zhao, Jianmin [1 ]
Li, Haiping [1 ]
Sun, Fucheng [1 ]
机构：
[1] Mech Engn Coll, Shijiazhuang 050003, Peoples R China
来源：
PROCEEDINGS OF THE 2016 4TH INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS AND COMPUTING TECHNOLOGY | 2016年 / 60卷
关键词：
Clustering analysis; bearing; fault pattern; time domain feature parameters; K-MEANS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, the clustering analysis is used to distinguish bearing fault pattern. Some time domain feature parameters are extracted from vibration signal, and the combination of three feature parameters are chosen from these feature parameters for the clustering analysis. The Euclidean distance is used to calculate the distance of point-to-center. After validation, the effect of clustering analysis is effective to distinguish the bearing fault pattern, and the best combination of feature parameters for fault pattern recognition by clustering analysis is found.
引用
收藏
页码：422 / 427
页数：6
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