Roller Bearing Fault diagnosis Based on EMD Sample Entropy

摘要：

A roller bearing fault diagnosis method has been proposed based on Empirical Mode Decomposition (EMD) sample entropy (SampEn), in order to deal with the nonlinearity existing in bearing vibration signals. Firstly, original vibration signals are decomposed into a number of intrinsic mode functions (IMFs). Then the SampEn values of first numbers of IMFs that contained the most dominant fault information are calculated and serve as the feature vector for bearing fault diagnosis. The analysis results from EMD SampEn of different vibration signals show that the SampEn is an effective feature. Finally, SVM is used to identify the work condition of the roller bearing. Experimental results with CWRU data show that the diagnosis approach based on SVM by using EMD SampEn as features can identify roller bearing fault patterns accurately.

关键词： fault diagnosis empirical mode decomposition sample entropy

作者: Zhihong Zhao Shaopu Yang

作者单位: School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University School of Comp School of Computing and Informatics, Shijiazhuang Tiedao University Shijiazhuang, China

会议类型: 国际会议

会议名称: 2010 4th International Conference on Intelligent Information Techonlogy Application(第四届智能信息技术应用国际学术研讨会 IITA 2010)

会议地点: 秦皇岛

会议语种:英文

页码: 72-76

在线出版日期: 2010-11-05（万方平台首次上网日期，不代表论文的发表时间）