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Volume 7 Issue 3
Apr.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(3): 800-811
Qinqin Zhu, "Latent Variable Regression for Supervised Modeling and Monitoring," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 800-811, May 2020. doi: 10.1109/JAS.2020.1003153
Citation: Qinqin Zhu, "Latent Variable Regression for Supervised Modeling and Monitoring," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 800-811, May 2020. doi: 10.1109/JAS.2020.1003153
shu

Latent Variable Regression for Supervised Modeling and Monitoring

doi: 10.1109/JAS.2020.1003153
Funds:  This work was supported by the Chemical Engineering Department at the University of Waterloo
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    Abstract
  • A latent variable regression algorithm with a regularization term (rLVR) is proposed in this paper to extract latent relations between process data X and quality data Y . In rLVR, the prediction error between X and Y is minimized, which is proved to be equivalent to maximizing the projection of quality variables in the latent space. The geometric properties and model relations of rLVR are analyzed, and the geometric and theoretical relations among rLVR, partial least squares, and canonical correlation analysis are also presented. The rLVR-based monitoring framework is developed to monitor process-relevant and quality-relevant variations simultaneously. The prediction and monitoring effectiveness of rLVR algorithm is demonstrated through both numerical simulations and the Tennessee Eastman (TE) process.

     

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