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.

典型的相关分析（CCA）和对潜在结构的投影（PLS）是用于过程建模和监视的流行统计方法。CCA仅关注相关结构，而PLS专注于最大化过程变量X和质量变量Y之间的协方差。本文提出了一种动态正则化潜在变量回归（DrLVR）算法，用于动态数据建模和监控。DrLVR旨在最大程度地将质量变量投影到过程变量的动态潜在空间上。正则化术语已合并到DrLVR中以处理共线性问题。还开发了基于DrLVR模型的动态监控方案。数值模拟和田纳西州伊士曼过程数据均被用来证明DrLVR的有效性。