Chiller gradual fault detection based on Independent Component Analysis - Details

Author：

Wang, Pu (Wang, Pu.) | Xin, Jiaojiao (Xin, Jiaojiao.) | Gao, Xuejin (Gao, Xuejin.) (Scholars：高学金) | Zhang, Yachao (Zhang, Yachao.)

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Abstract：

Aiming　at　the　chiller　process　variables　cannot　be　strictly　obey　the　Gauss　distribution,　and　the　large　number　of　variables　between　the　serious　correlation,　this　paper　describes　a　fault　detection　method　to　detect　the　faults　of　chiller.　Independent　Component　Analysis(ICA)　approach　is　used　to　extract　the　correlation　of　variables　of　chiller　and　reduce　the　dimension　of　measured　data.　A　ICA-based　method　model　is　built　to　determine　the　thresholds　of　statistics　and　calculate　statistics　I2and　SPE,　which　are　used　to　check　if　a　fault　occurs　in　chiller.　The　method　is　validated　using　the　laboratory　data　from　ASHRAE　RP-1043　and　compared　with　Principle　Component　Analysis　(PCA).　Results　show　that　the　ICA-based　method　has　better　fault　detection　performance　of　chiller.　It　has　very　good　sensitivity　for　early　fault　and　can　effectively　reduce　the　false　alarm　rate.　©　2015　IEEE.

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Author Community：

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

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Year： 2015

Page： 2422-2426

Language： English

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 3

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 11

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