A New Method of Dynamic Latent-Variable Modeling for Process Monitoring

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)6438-6445
Number of pages8
Journal / PublicationIEEE Transactions on Industrial Electronics
Issue number11
Online published21 Jan 2014
Publication statusPublished - Nov 2014
Externally publishedYes


Dynamic principal component analysis (DPCA) is widely used in the monitoring of dynamic multivariate processes. In traditional DPCA where a time window is used, the dynamic relations among process variables are implicit and difficult to interpret in terms of variables. To extract explicit latent variables that are dynamically correlated, a dynamic latent-variable model is proposed in this paper. The new structure can improve the modeling and the interpretation of dynamic processes and enhance the performance of monitoring. Fault detection strategies are developed, and contribution analysis is available for the proposed model. The case study on the Tennessee Eastman Process is used to illustrate the effectiveness of the proposed methods.

Research Area(s)

  • Contribution plots, dynamic latent-variable (DLV) model, dynamic principal component analysis (DPCA), process monitoring and fault diagnosis, subspace identification method