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Correlated and weakly correlated fault detection based on variable division and ICA
Li S(李帅); Zhou XF(周晓锋); Pan FC(潘福成); Shi HB(史海波); Li KT(李开拓); Wang ZW(王中伟)
作者部门数字工厂研究室
关键词Fault Detection Monitoring Variable Division Correlated And Weakly Correlated Variables Independent Component Analysis
发表期刊Computers and Industrial Engineering
ISSN0360-8352
2017
卷号112页码:320-335
收录类别SCI ; EI
EI收录号20173604128046
WOS记录号WOS:000413126700026
产权排序1
资助机构Science and Technology Project of Liaoning Province (2015106015) and the Key Laboratory of Network Control System, Chinese Academy of Sciences.
摘要In many industrial processes, the correlations of multiple variables are complicated. Some variables are correlated and some are weakly correlated with others, which should be considered in process modelling and fault detection. This paper proposes a correlated and weakly correlated fault detection approach, which is mainly based on variable division and independent component analysis (ICA). A few variables are weakly correlated with others and fault detection should be implemented separately for correlated and weakly correlated subspaces. Firstly, variable division based on weighted proximity measure is presented to obtain correlated and weakly correlated variables. Then, ICA is used for fault detection in correlated subspace and weakly correlated subspace, which needs not kernel mapping or kernel parameter setting. Finally, comprehensive statistics are built based on different subspaces. The proposed method considers the correlated and weakly correlated characteristics of variables and the advantages of ICA in handling weakly correlated variables. The monitoring results of the numerical system and Tennessee Eastman (TE) process have been used to demonstrate effectiveness and superiority of the proposed approach.
语种英语
WOS标题词Science & Technology ; Technology
WOS类目Computer Science, Interdisciplinary Applications ; Engineering, Industrial
关键词[WOS]INDEPENDENT COMPONENT ANALYSIS ; MAXIMAL INFORMATION COEFFICIENT ; MONITORING BATCH PROCESSES ; NONLINEAR PROCESSES ; CONTROL CHART ; DISTANCE CORRELATION ; STATISTICAL-ANALYSIS ; MULTIMODE PROCESSES ; BAYESIAN-INFERENCE ; PROCESS DISPERSION
WOS研究方向Computer Science ; Engineering
引用统计
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/20964
专题数字工厂研究室
通讯作者Li S(李帅)
作者单位1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
推荐引用方式
GB/T 7714
Li S,Zhou XF,Pan FC,et al. Correlated and weakly correlated fault detection based on variable division and ICA[J]. Computers and Industrial Engineering,2017,112:320-335.
APA Li S,Zhou XF,Pan FC,Shi HB,Li KT,&Wang ZW.(2017).Correlated and weakly correlated fault detection based on variable division and ICA.Computers and Industrial Engineering,112,320-335.
MLA Li S,et al."Correlated and weakly correlated fault detection based on variable division and ICA".Computers and Industrial Engineering 112(2017):320-335.
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