Latent Variable Regression for Supervised Modeling and Monitoring  

作　　者：Qinqin Zhu 

机构地区：[1]Department of Chemical Engineering,University of Waterloo,ON N2L 3G1,Canada

出　　处：《IEEE/CAA Journal of Automatica Sinica》2020年第3期800-811,共12页自动化学报（英文版）

基　　金：supported by the Chemical Engineering Department at the University of Waterloo。

摘　　要：A latent variable regression algorithm with a regularization term(r LVR) 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 r LVR, 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.

关 键 词：Data ANALYTICS inferential MONITORING LATENT VARIABLE regression REGULARIZATION 

分 类 号：O29[理学—应用数学] TP277[理学—数学]