Bearing Fault Diagnosis Based on Rough Set

摘要：

Bearing defects were categorized as localized and distributed. For on-line bearing fault diagnosis, in this paper, the time-domain kurtosis calculation and the frequency domain wavelet analysis were used to extract the transitory features of non-stationary vibration signal produced by the bearing distributed defects. To distributed defects, bearing fault diagnosis was built on the reducing decision based on rough set. According to the information entropy and importance of attribute, adding the most important it to the reduction set, the optimization and minimum reduction set can be output by the attribute reduction algorithm, without computing the core of the attribute set. The results show that the proposed method was effective, and the method provides a promising technique of online bearings condition monitoring for practical applications.

关键词： fault diagnosis bearings rough set information entropy

作者: ChenXin Yuhua Chen Guofeng Wang HuDong

作者单位: Information Science and Technology College, Dalian Maritime University, Dalian, PR China Technical-Testing Center, Wafangdian Bearing Group Corporation, Dalian, PR China

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)

会议地点: 大连

会议语种:英文

页码: 2387-2390

在线出版日期: 2010-07-05（万方平台首次上网日期，不代表论文的发表时间）