Fault detection of chiller based on improved KPCA - 文章详情页

作者：

Zhang, Nanhua (Zhang, Nanhua.) | Gao, Xuejin (Gao, Xuejin.) (学者：高学金) | Li, Yafen (Li, Yafen.) | Wang, Pu (Wang, Pu.)

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EI Scopus

摘要：

Principal　component　analysis　(PCA)　is　a　common　fault　detection　method.　But　it　is　difficult　to　get　high　accuracy·　if　it　is　applied　to　complex　nonlinear　system.　Faced　with　complex　system　such　as　chiller·　this　paper　proposes　using　kernel　principal　component　analysis　(KPCA)　for　fault　detection.　But·　the　selection　of　kernel　parameters　is　a　problem　in　the　implement　of　KPCA　algorithm.　Genetic　algorithm　(GA)　is　used　to　determine　the　kernel　parameter　through　minimizing　false　alarm　rate　and　maximizing　detection　rate.　This　method　is　verified　by　ASHRAE　1043-RP　data.　The　results　show　that　it　is　better　than　PCA.　And　it　can　improve　the　accuracy　of　fault　detection.　©　2016　IEEE.

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作者机构：

[ 1 ] [Zhang, Nanhua]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Zhang, Nanhua]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
[ 3 ] [Zhang, Nanhua]Beijing Laboratory for Urban Mass Transit, Beijing; 100124, China
[ 4 ] [Zhang, Nanhua]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
[ 5 ] [Gao, Xuejin]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 6 ] [Gao, Xuejin]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
[ 7 ] [Gao, Xuejin]Beijing Laboratory for Urban Mass Transit, Beijing; 100124, China
[ 8 ] [Gao, Xuejin]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
[ 9 ] [Li, Yafen]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 10 ] [Li, Yafen]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
[ 11 ] [Li, Yafen]Beijing Laboratory for Urban Mass Transit, Beijing; 100124, China
[ 12 ] [Li, Yafen]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
[ 13 ] [Wang, Pu]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 14 ] [Wang, Pu]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
[ 15 ] [Wang, Pu]Beijing Laboratory for Urban Mass Transit, Beijing; 100124, China
[ 16 ] [Wang, Pu]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China

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