Application Research of Kalman Filter and SVM Applied to Condition Monitoring and Fault Diagnosis

摘要：

　　In the condition monitoring and fault diagnosis,useful information about the incipient fault features in the measured signal is always corrupted by noise.Fortunately,the Kalman filtering technique can filter the noise effectively,and the impending system fault can be revealed to prevent the system from malfunction.This paper has discussed recent progress of the Kalman filters for the condition monitoring and fault diagnosis.A case study on the rolling bearing condition monitoring and fault diagnosis using Kalman filter and support vector machine (SVM) has been presented.The analysis result showed that the integration of the Kalman filter and SVM was feasible and reliable for the rolling bearing condition monitoring and fault diagnosis and the fault detection rate was over 96.5%.

关键词： Condition monitoring Fault diagnosis Kalman filtering SVM

作者: Ke Li Yuelei Zhang Zhixiong Li

作者单位: Department of CNC Technology, Yantai Engineering & Technology College, Yantai, China School of Energy & Power Engineering, Wuhan University of Technology; Unit 94270 of PLA,China

会议类型: 国际会议

会议名称: the Second International Conference on Frontiers of Manufacturing and Design Science(第二届制造与设计科学国际会议(ICFMD 2011))

会议地点: 台湾

会议语种:英文

页码: 268-272

在线出版日期: 2011-12-11（万方平台首次上网日期，不代表论文的发表时间）