Fault diagnosis of roller bearing feature subset select based on greedy algorithm

Because RST's ability 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 redund...

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Bibliographic Details
Main Authors: Min yong, Guo yi-nan, Yan Jun-rong
Format: Conference Proceeding
Language:chi ; eng
Subjects:
Fault diagnosis
Feature Subset Select
Greedy Algorithm
Greedy algorithms
Rolling bearings
RST
Testing
Virtual colonoscopy
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Summary:Because RST's ability 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.
ISSN:1948-9439
1948-9447
DOI:10.1109/CCDC.2010.5498468