Latent variable regression for supervised modeling and monitoring

摘要

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.