Dynamic latent variable modeling for statistical process monitoring

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• 作者：Gang Li^* ; ^{dlg00@mails.tsinghua.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Baosheng Liu^** ; ^{liubaosheng1981@tsinghua.org.cn" class="auth_mail" title="E-mail the corresponding author} ; S. Joe Qin^*** ; ^{sqin@usc.edu" class="auth_mail" title="E-mail the corresponding author} ; Donghua Zhou^* ; ^{zdh@tsinghua.edu.cn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：dynamic latent variable model ; dynamic principal component analysis ; subspace method ; process monitoring
• 刊名：IFAC-PapersOnLine
• 出版年：2011
• 出版时间：January 2011
• 年：2011
• 卷：44
• 期：1
• 页码：12886-12891
• 全文大小：268 K

文摘

Dynamic principal component analysis (DPCA) has been widely used in the monitoring of dynamic multivariate processes. In traditional DPCA, the dynamic relationship between process variables are implicit and hard to interpret. To extract explicit latent factors that are dynamically correlated, a new dynamic latent variable model is proposed. The new structure can improve modeling of dynamic data and enhance the process monitoring performance. Fault detection indices are developed based on the proposed model. A case study is given to illustrate the effectiveness of the proposed new dynamic factor model.