Fault Diagnosis of Chiller Based on Independent Component Analysis and Least Squares Support Vector Machine - 文章详情页

作者：

Wang, P. (Wang, P..) | Xin, J. (Xin, J..) | Gao, X. (Gao, X..) | Zhang, N. (Zhang, N..) (学者：张楠)

收录：

Scopus PKU CSCD

摘要：

Chiller　is　a　complex　system　in　which　the　correlation　between　variables　is　serious.　When　a　fault　occurs,　the　symptoms　and　causes　of　the　chiller　show　diversity,　leading　to　great　difficulty　in　fault　diagnosis　of　the　chiller.　To　reduce　the　data　redundancy　and　improve　the　efficiency　of　the　fault　diagnosis,　a　fault　diagnosis　method　for　chiller　based　on　independent　component　analysis　(ICA)　and　least　squares　support　vector　machine　(LSSVM)　was　proposed.　ICA　was　used　to　extract　the　correlation　of　variables　of　the　chiller　and　feature　extraction　was　made　which　was　served　as　input　parameters　of　LSSVM　in　order　to　identify　the　chiller＇s　fault　type.　The　method　was　validated　by　using　the　laboratory　data　from　the　ventilation　and　air　conditioning　training　platform　of　a　subway　station　in　Beijing　and　the　method　was　compared　with　the　traditional　fault　diagnosis　method　of　chiller.　Results　show　that　the　method　is　better　than　the　traditional　method.　It　can　effectively　extract　the　data　from　the　high-order　statistical　information　and　improve　the　efficiency　of　fault　diagnosis.　©　2017,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.

关键词：

Chiller; Fault diagnosis; Indepedent component analysis (ICA); Least squares support vector machine (LSSVM)

作者机构：

[ 1 ] [Wang, P.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Wang, P.]Beijing Laboratory For Urban Mass Transit, Beijing, 100124, China
[ 3 ] [Wang, P.]The Ministry of Education P. R. C. Engineering Research Center of Digital Community, Beijing, 100124, China
[ 4 ] [Wang, P.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China
[ 5 ] [Xin, J.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 6 ] [Xin, J.]Beijing Laboratory For Urban Mass Transit, Beijing, 100124, China
[ 7 ] [Xin, J.]The Ministry of Education P. R. C. Engineering Research Center of Digital Community, Beijing, 100124, China
[ 8 ] [Xin, J.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China
[ 9 ] [Gao, X.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 10 ] [Gao, X.]Beijing Laboratory For Urban Mass Transit, Beijing, 100124, China
[ 11 ] [Gao, X.]The Ministry of Education P. R. C. Engineering Research Center of Digital Community, Beijing, 100124, China
[ 12 ] [Gao, X.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China
[ 13 ] [Zhang, N.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 14 ] [Zhang, N.]Beijing Laboratory For Urban Mass Transit, Beijing, 100124, China
[ 15 ] [Zhang, N.]The Ministry of Education P. R. C. Engineering Research Center of Digital Community, Beijing, 100124, China
[ 16 ] [Zhang, N.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China

通讯作者信息：

[Gao, X.]Faculty of Information Technology, Beijing University of TechnologyChina

电子邮件地址：

gaoxuejin@bjut.edu.cn

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