An Improved Kernel Method for Fault Diagnosis

摘要：

Kernel fisher discriminant analysis (KFDA) has been widely used in fault diagnosis. In this paper, a feature vector selection (FVS) scheme based on a geometrical consideration is given to reduce the computational complexity of KFDA when the number of samples becomes large. Experimental results show the effectiveness of our method.

作者: J. F. Cui G. S. Guo M. X. Miao S. X. Liu

作者单位: Department of Mechanical and Electrical Engineering,Zhengzhou Institute of Aeronutical Industry Management.Zhengzhou,450015,China

会议类型: 国际会议

会议名称: The 2nd International Symposium on Systems and Control in Aeronautics and Astronautics(第二届航空航天系统与控制国际会议 ISSCAA 2008)

会议地点: 深圳

会议语种:英文

页码: 186-189

在线出版日期: 2008-12-10（万方平台首次上网日期，不代表论文的发表时间）