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 language | English |
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Pages (from-to) | 12886-12891 |
Number of pages | 6 |
Journal | IFAC Proceedings Volumes |
Volume | 44 |
Issue number | 1 |
DOIs | |
Publication status | Published - Jan 2011 |
Externally published | Yes |
Event | 18th World Congress of the International Federation of Automatic Control (IFAC 2011) - , Italy Duration: 28 Aug 2011 → 2 Sept 2011 |
Keywords
- Dynamic latent variable model
- Dynamic principal component analysis
- Process monitoring
- Subspace method