DocumentCode :
569090
Title :
An Improved FVS-KPCA Method of Fault Detection on TE Process
Author :
Zhao Xiaoqiang ; Wang Xinming ; Yang Wu
Author_Institution :
Coll. of Electr. & Inf. Eng., Lanzhou Univ. of Technol., Lanzhou, China
fYear :
2012
fDate :
July 31 2012-Aug. 2 2012
Firstpage :
186
Lastpage :
189
Abstract :
For complex nonlinear systems of chemical industry process, traditional kernel principal component analysis (KPCA) methods are very difficult to calculate the kernel matrix for fault detection with large sample sets. So an improved fault detection method based on feature vector selection-KPCA (FVS-KPCA) is developed. This method can evidently reduce calculational complexity of fault detection and is applied to the benchmark of Tennessee Eastman (TE) processes. The simulation results show that the proposed method can effectively improve the speed of fault detection.
Keywords :
chemical industry; fault diagnosis; principal component analysis; vectors; TE process; Tennessee Eastman processes; calculational complexity; chemical industry process; complex nonlinear systems; fault detection; feature vector selection-KPCA; improved FVS-KPCA method; kernel principal component analysis; Eigenvalues and eigenfunctions; Equations; Fault detection; Kernel; Mathematical model; Principal component analysis; Vectors; Fault Detection; Feature Vector Selection; Kernel Principal Component Analysis; Tennessee EastmanPprocess;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Manufacturing and Automation (ICDMA), 2012 Third International Conference on
Conference_Location :
GuiLin
Print_ISBN :
978-1-4673-2217-1
Type :
conf
DOI :
10.1109/ICDMA.2012.45
Filename :
6298285
Link To Document :
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