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.
|Number of pages||6|
|Journal||IFAC Proceedings Volumes|
|Publication status||Published - Jan 2011|
|Event||18th World Congress of the International Federation of Automatic Control (IFAC 2011) - , Italy|
Duration: 28 Aug 2011 → 2 Sept 2011
Bibliographical noteISBN: 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.
- Dynamic latent variable model
- Dynamic principal component analysis
- Process monitoring
- Subspace method