Fault Diagnosis of Rolling Bearing Based on Wavelet Packet Transform and Support Vector Machine

摘要：

In this paper, fault diagnosis approach to roiling bearing based on wavelet packet transform and support vector machine is proposed. At first, feature vectors are extracted from the non-stationary vibration signals by means of wavelet packet transform. Then support sector sachine algorithm is used to fault identification and classification of rolling bearing. The experiments show that, as for limited fault samples, support vector machine classifier has a better classification efficiency than BP neural network classifier.

关键词： rolling element bearing fault diagnosis wavelet packet transform support vector machine

作者: Yang Zhengyou Peng Tao Li Jianbao Yang Huibin Jiang Haiyan

作者单位: College of Electrical Engineering,Hunan University of Technology Zhuzhou,China

会议类型: 国际会议

会议名称: 2009 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2009)(2009年检测技术与机械自动化国际会议)

会议地点: 长沙

会议语种:英文

页码: 650-653

在线出版日期: 2009-04-11（万方平台首次上网日期，不代表论文的发表时间）