Fault detection of chiller based on improved KPCA - Details

Author：

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

Indexed by：

EI Scopus

Abstract：

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.

Keyword：

Author Community：

[ 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

Reprint Author's Address：

Email：

Show more details

Related Keywords：

Source ：

Year： 2016

Page： 2951-2955

Language： English

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 8

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 1

Affiliated Colleges：

信息学部

Get Fulltext

DOI Library Discovery Baidu Scholar Search Engineering Village

Type
Departments

All Years Choose Year From to