Fault Diagnosis of Roller Bearing Feature Subset Select Based on Greedy Algorithm

被引：0

作者：

Min Yong ^{[1
,2
]}

Guo Yi-nan ^{[1
]}

Yan Jun-rong ^{[2
]}

机构：

[1] China Univ Min & Technol, Coll Informat & Elect Engn, Xuzhou 221008, Jiangsu, Peoples R China

[2] Xuzhou Normal Univ, Mech & Elect Engn Inst, Xuzhou 221116, Peoples R China

来源：

2010 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-5 | 2010年

关键词：

RST; Greedy Algorithm; Feature Subset Select; Fault Diagnosis;

D O I：

10.1109/CCDC.2010.5498468

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Because RST 's ablity of data reduction, feature subset selection was translated into the process of data reduction. The condition attributes and decidation attributes of the diagnosis system were reducted, and we received the best training swatch which were cleared up the information of redundance and repetition. Greedy algorithm is a method of discretion and a algorithm of attribute reduction. In the article, fault diagnosis data of roller bearing was discreted and was reducted its attribute. The simple and reliable diagnosis rulers were received, and testing samples ralidated the reliability of the rulers.

引用

页码：3881 / +

页数：2