Dynamic latent variable regression for inferential sensor modeling and monitoring

Qinqin ZHU, S. Joe QIN, Yining DONG*

*Corresponding author for this work

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

40 Citations (Scopus)

Abstract

Canonical correlation analysis (CCA) and projection to latent structures (PLS) are popular statistical approaches for process modeling and monitoring. CCA focuses on the correlation structure only, while PLS focuses on maximizing the covariance between process variables X and quality variables Y. In this article, a dynamic regularized latent variable regression (DrLVR) algorithm is proposed for dynamic data modeling and monitoring. DrLVR aims to maximize the projection of quality variables on the dynamic latent spaces of the process variables. A regularization term is incorporated into DrLVR to handle the collinearity issues. The dynamic monitoring scheme based on the DrLVR model is also developed. Both numerical simulations and the Tennessee Eastman process data are employed to demonstrate the effectiveness of DrLVR.
Original languageEnglish
Article number106809
JournalComputers and Chemical Engineering
Volume137
DOIs
Publication statusPublished - 9 Jun 2020
Externally publishedYes

Bibliographical note

This work was supported in part by the Texas-Wisconsin-California Control Consortium. The first author would also like to acknowledge the financial support provided by the Chemical Engineering Department at the University of Waterloo.

Keywords

  • Data analytics
  • Dynamic inferential monitoring
  • Dynamic inferential sensors
  • Dynamic latent variable regression
  • Regularization

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