Fault Diagnosis of Roller Bearing Using Dual-Tree Complex Wavelet Transform,Rough Set and Neural Network
In a complex field environment for modern mechanical equipment,how to identify all kinds of operational status of the rolling element bearings fastly and accurately is very important and necessary.A novel approach to automated diagnosis is introduced,which is based on feature extraction with the Dual-Tree Complex Wavelet Transform (DT-CWT),then attribute reduction with rough set theory and finally pattern recognition with Artificial Neural Network.In our experiment,4 kinds of states on a rolling element bearing test table,including normal,pitting on inner ring,pitting on outer ring and pitting on rolling element,are adopted.The experimental results indicate that the proposed feature extraction and automated diagnosis method can extract significant feature sets from signal,and can accurately distinguish many fauR pattern,and has some practical value for the on-line condition monitoring of modern industrial demands.
Dual-Tree Complex Wavelet Transform rough set theory Neural Network rolling element bearings fault diagnosis
Zhixin Chen Lixin Gao
Logistics School,Beijing Wuzi University,Beijing 101149,PR China Key Laboratory of Advanced Manufacturing Technology,Beijing University of Technology,Chao Yang Distr
国际会议
杭州
英文
1197-1200
2013-03-22（万方平台首次上网日期，不代表论文的发表时间）