DocumentCode
2636155
Title
An improved Kernel method for fault diagnosis
Author
Cui, I.F. ; Guo, G.S. ; Miao, M.X. ; Liu, S.X.
Author_Institution
Dept. of Mech. & Electr. Eng., Zhengzhou Inst. of Aeronutical Ind. Manage., Zhengzhou
fYear
2008
fDate
10-12 Dec. 2008
Firstpage
1
Lastpage
4
Abstract
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.
Keywords
computational complexity; fault diagnosis; manufacturing processes; statistical analysis; computational complexity; fault diagnosis; feature vector selection scheme; improved Kernel method; kernel Fisher discriminant analysis; Computational complexity; Engineering management; Fault diagnosis; Feature extraction; Independent component analysis; Kernel; Manufacturing processes; Principal component analysis; Scattering; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems and Control in Aerospace and Astronautics, 2008. ISSCAA 2008. 2nd International Symposium on
Conference_Location
Shenzhen
Print_ISBN
978-1-4244-3908-9
Electronic_ISBN
978-1-4244-2386-6
Type
conf
DOI
10.1109/ISSCAA.2008.4776167
Filename
4776167
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