Study of Rolling Bearing SVM Pattern Recognition Based on Correlation Dimension of IMF

摘要：

A method of pattern recognition based on correlation of intrinsic mode function (IMF) and Support Vector Machine (SVM) was proposed.Firstly, the rolling bearing vibration signal was decomposed into a finit series of IMFS by EMD. Secondly, useful IMFS which contained main fault information were chosen through correlation coefficient threshold filtering method. Finally the correlation dimensions of the main IMFS were computed and served as input characteristic parameters of SVM classifiers to classify normal state,outner and inner fault of the rolling bearing The method has been applied on pattern recognition of the NO. 6205 rolling bearing . The results show that the proposed approach can identify the working state and fault pattern for the bearing system accurately and effectively and provide a reliable way for the fault diagnosis of mechanical device in the electrical power system.

关键词： correlation dimensions SVM correlation coefficient IMF pattern recognition

作者: Jiang Qing Li Ting Yao Yan Cai Jinhui

作者单位: College of Metrology and Measuring Engnieering,China Jiliang University,Hangzhou 310018,China

会议类型: 国际会议

会议名称: 2012 International Conference on Intelligent System Design and Engineering Applications(2012年智能系统设计与工程应用国际会议 ISDEA 2012)

会议地点: 三亚

会议语种:英文

页码: 1132-1135

在线出版日期: 2012-01-06（万方平台首次上网日期，不代表论文的发表时间）