Chiller gradual fault detection based on Independent Component Analysis - 文章详情页

作者：

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

收录：

EI Scopus

摘要：

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.

关键词：

作者机构：

[ 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

通讯作者信息：

电子邮件地址：

查看成果更多字段

成果类型
所属机构

所有年份指定年份从至