文献类型：期刊文献

中文题名：基于协调混合概率主元分析的多模态化工过程故障检测（英文）

英文题名：An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

作者：杨雅伟[1];马玉鑫[1];宋冰[1];侍洪波[1]

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2015

卷号：23

期号：8

起止页码：1357

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：中国化学工程学报（英文版）

收录：CSTPCD;;EI(收录号：20152701005588);Scopus;WOS:【SCI-EXPANDED(收录号:WOS:000361836600014)】；CSCD:【CSCD2015_2016】；

基金：Supported by the National Natural Science Foundation of China (61374140) and Shanghai Pujiang Program (12PJ1402200).

语种：中文

中文关键词：概率主成分分析;故障检测;多模式;混合;化学过程;对齐;连续搅拌釜式反应器;局部模型

外文关键词：Multimode process monitoring Mixture probabilistic principal component analysis Model alignment Fault detection

摘要：A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.
A novel approach named aligned mixture probabilistic principal component analysis（AMPPCA） is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis（MPPCA）. As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.