Dynamic latent variable modeling for statistical process monitoring

Gang LI*, Baosheng LIU, S. Joe QIN, Donghua ZHOU

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

38 Citations (Scopus)

Abstract

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. © 2011 IFAC.
Original languageEnglish
Pages (from-to)12886-12891
Number of pages6
JournalIFAC Proceedings Volumes
Volume44
Issue number1
DOIs
Publication statusPublished - Jan 2011
Externally publishedYes
Event18th World Congress of the International Federation of Automatic Control (IFAC 2011) - , Italy
Duration: 28 Aug 20112 Sept 2011

Bibliographical note

ISBN: 9783902661937 <br/>This work was supported by National 973 project under grants 2010CB731800 & 2009CB320602, the NSFC under grants 60721003, 60736026 & 60931160440, and the Texas-Wisconsin-California control consortium.

Keywords

  • Dynamic latent variable model
  • Dynamic principal component analysis
  • Process monitoring
  • Subspace method

Fingerprint

Dive into the research topics of 'Dynamic latent variable modeling for statistical process monitoring'. Together they form a unique fingerprint.

Cite this