The Application of PCA and SVM in Rolling Bearing Fault Diagnosis

摘要：

　　The key to the fault diagnosis is feature extracting and fault pattern classifying.Principal components analysis (PCA) and support vector machine (SVM) method are introduced to recognize the fault pattern of the rolling bearing in this paper.Multidimensional correlated variable is converted into low dimensional independent eigenvector by means of PCA.The pattern recognition and the nonlinear regression are achieved by the method of SVM.In the light of the feature of vibrating signals,eigenvector is obtained using PCA,fault diagnosis of rolling bearing is recognized correspondingly using SVM fault classifier.Theory and experiment show that the recognition of fault diagnosis of rolling bearing based on PCA and SVM theory is available in the fault pattern recognition and provides a new approach to intelligent fault diagnosis.

关键词： Principal components analysis(PCA) Support vector machine(SVM) Rolling bearing Fault diagnosis

作者: Meng Li

作者单位: College of Mechanical Engineering, Changchun University, Changchun, Jilin, 130022, China

会议类型: 国际会议

会议名称: the 2012 International Conference on Frontiers of Advanced Materials and Engineering Technology (2012年先进材料与工程技术国际会议(FAMET 2012))

会议地点: 厦门

会议语种:英文

页码: 1163-1166

在线出版日期: 2012-01-04（万方平台首次上网日期，不代表论文的发表时间）