典型文献
Cycle temporal algorithm-based multivariate statistical methods for fault diagnosis in chemical processes
文献摘要：
Multivariate statistical process monitoring methods are often used in chemical process fault diagnosis.In this article,(I)the cycle temporal algorithm(CTA)combined with the dynamic kernel principal compo-nent analysis(DKPCA)and the multiway dynamic kernel principal component analysis(MDKPCA)fault detection algorithms are proposed,which are used for continuous and batch process fault detections,respectively.In addition,(Ⅱ)a fault variable identification model based on reconstructed-based contribu-tion(RBC)model that paves the way for determining the cause of the fault are proposed.The proposed fault diagnosis model was applied to Tennessee Eastman(TE)process and penicillin fermentation process for fault diagnosis.And compare with other fault diagnosis methods.The results show that the proposed method has better detection effects than other methods.Finally,the reconstruction-based contribution(RBC)model method is used to accurately locate the root cause of the fault and determine the fault path.
文献关键词：
中图分类号：
作者姓名：
Jiaxin Zhang;Wenjia Luo;Yiyang Dai;Yuman Yao
作者机构：
School of Chemistry and Chemical Engineering,Southwest Petroleum University,Chengdu 610500,China;School of Chemical Engineering,Sichuan University,Chengdu 610065,China
文献出处：
引用格式：
[1]Jiaxin Zhang;Wenjia Luo;Yiyang Dai;Yuman Yao-.Cycle temporal algorithm-based multivariate statistical methods for fault diagnosis in chemical processes)[J].中国化学工程学报（英文版）,2022(07):54-70
A类：
DKPCA,MDKPCA
B类：
Cycle,temporal,multivariate,statistical,methods,fault,diagnosis,chemical,processes,Multivariate,monitoring,often,used,In,this,article,cycle,CTA,combined,dynamic,kernel,principal,analysis,multiway,component,algorithms,proposed,which,continuous,batch,detections,respectively,addition,variable,identification,model,reconstructed,RBC,that,paves,determining,cause,was,applied,Tennessee,Eastman,TE,penicillin,fermentation,And,compare,other,results,show,has,better,effects,than,Finally,reconstruction,contribution,accurately,locate,root,determine,path
AB值：
0.468323
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