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Dynamic Process Monitoring Based on Variational Bayesian Canonical Variate Analysis by Jiaxin Yu; Lingjian Ye; Le Zhou; Zeyu Yang; Feifan Shen; Zhihuan Song is a Engineering article available to read on EtoBox.

What is Dynamic Process Monitoring Based on Variational Bayesian Canonical Variate Analysis about?

Fault detection and fault identification are consecutive steps of multivariate statistical process monitoring. In recent years, increasing attention has been paid to process dynamics. For dynamic process modeling, canonical variate analysis (CVA) extracts process dynamics effectively. However, process noises are not well analyzed in traditional CVA and corresponding fault identification methods are less studied. To solve these issues, a variational Bayesian CVA (VBCVA) model is proposed for dynamic process monitoring. Through a probabilistic perspective, the inevitable noises in realistic industrial processes can be captured in the new model. Moreover, the proposed model is further extended in the variational Bayesian framework to overcome the common problems in probabilistic methods. Besides, an improved fault identification approach based on fault relevance is introduced, which avoids the smearing effect caused by data reconstruction. Finally, the feasibility of the proposed process monitoring scheme is verified on the TE benchmark and a real wastewater treatment process.

Who reads Dynamic Process Monitoring Based on Variational Bayesian Canonical Variate Analysis?

Author

Jiaxin Yu; Lingjian Ye; Le Zhou; Zeyu Yang; Feifan Shen; Zhihuan Song

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2022

Language

EN

Field

Engineering (Physical Sciences)