Research on batch process monitoring method based on multi-way kernel entropy component analysis and angular structure statistic - 文章详情页

作者：

Wang, Pu (Wang, Pu.) | Li, Chunlei (Li, Chunlei.) | Gao, Xuejin (Gao, Xuejin.) (学者：高学金) | Chang, Peng (Chang, Peng.) | Qi, Yongsheng (Qi, Yongsheng.)

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摘要：

Aiming　at　monitoring　the　batch　process　with　complex　nonlinear　characteristic,　a　multi-way　kernel　entropy　component　analysis　(MKECA)　method　based　on　the　angle　structure　statistic　is　proposed.　In　this　method,　the　process　data　is　firstly　preprocessed,　and　then　the　principal　component　matrices　of　the　batch　process　data　are　extracted　by　KECA.　Research　shows　that　KECA　reveals　angular　structure　relating　to　the　Renyi　entropy　of　the　input　space　data　set,　and　angular　structure　statistic　is　constructed　using　the　principal　component　matrix　structure.　And　then　the　control　limits　are　calculated　by　the　kernel　density　estimation　algorithm.　Finally,　through　the　simulation　of　the　penicillin　fermentation　and　the　actual　production　process　of　recombinant,　the　experiment　results　show　that　the　proposed　method　effectively　uses　the　structural　information　of　the　principal　components　compared　to　the　traditional　method　of　process　monitoring.　So　error　rate　and　false　alarming　rate　are　significantly　lowered.　©　2017,　Science　Press.　All　right　reserved.

关键词：

Batch data processing Fault detection Matrix algebra Process control Process monitoring Statistics

作者机构：

[ 1 ] [Wang, Pu]The Information Department of Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Wang, Pu]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
[ 3 ] [Wang, Pu]Beijing Laboratory of Urban Rail Transit, Beijing; 100124, China
[ 4 ] [Wang, Pu]Beijing Laboratory of Computational Intelligence System, Beijing; 100124, China
[ 5 ] [Li, Chunlei]The Information Department of Beijing University of Technology, Beijing; 100124, China
[ 6 ] [Li, Chunlei]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
[ 7 ] [Li, Chunlei]Beijing Laboratory of Urban Rail Transit, Beijing; 100124, China
[ 8 ] [Li, Chunlei]Beijing Laboratory of Computational Intelligence System, Beijing; 100124, China
[ 9 ] [Gao, Xuejin]The Information Department of Beijing University of Technology, Beijing; 100124, China
[ 10 ] [Gao, Xuejin]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
[ 11 ] [Gao, Xuejin]Beijing Laboratory of Urban Rail Transit, Beijing; 100124, China
[ 12 ] [Gao, Xuejin]Beijing Laboratory of Computational Intelligence System, Beijing; 100124, China
[ 13 ] [Chang, Peng]The Information Department of Beijing University of Technology, Beijing; 100124, China
[ 14 ] [Chang, Peng]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
[ 15 ] [Chang, Peng]Beijing Laboratory of Urban Rail Transit, Beijing; 100124, China
[ 16 ] [Chang, Peng]Beijing Laboratory of Computational Intelligence System, Beijing; 100124, China
[ 17 ] [Qi, Yongsheng]School of Electric Power, Inner Mongolia University of Technology, Huhhot; 010051, China

通讯作者信息：

高学金
[gao, xuejin]the information department of beijing university of technology, beijing; 100124, china;;[gao, xuejin]engineering research center of digital community, ministry of education, beijing; 100124, china;;[gao, xuejin]beijing laboratory of computational intelligence system, beijing; 100124, china;;[gao, xuejin]beijing laboratory of urban rail transit, beijing; 100124, china

电子邮件地址：

gaoxuejin@bjut.edu.cn

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