Fault Diagnosis of Roller Bearing Based on PC A and Multi-class Support Vector Machine

摘要：

This paper discusses the fault features selection using principal com-ponent analysis and using multiclass support vector machine (MSVM) for bearing faults classification. The bearings vibration signal is obtained from ex-periment in accordance with the following conditions: normal bearing, bearing with inner race fault, bearing with outer race fault and bearings with balls fault. Statistical parameters of vibration signal such as mean, standard deviation, sample variance, kurtosis, skewness, etc, are processed with principal component analysis (PCA) for extracting the optimal features and reducing the dimension of original features. The multi-class classification algorithm of support vector machine (SVM), one against one strategy, is used for bearing multi-class fault diagnosis. The performance of the method proposed was high accurate and efficient.

关键词： fault diagnosis principal component analysis features selection multi-class support vector machine.

作者: Guifeng Jia Shengfa Yuan Chengwen Tang

作者单位: College of Engineering, Huazhong Agricultural University, Wuhan 430070, P.R. China

会议类型: 国际会议

会议名称: The 4th IFIP International on Computer and Computing Technologies in Agriculture and the 4th Symposium on Development of Rural Information(第四届国际计算机及计算机技术在农业中的应用研讨会暨第四届中国农业信息化发展论坛 CCTA 2010)

会议地点: 南昌

会议语种:英文

页码: 198-205

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）