Comparison of variable selection methods for PLS-based soft sensor modeling. (February 2015)

• Record Type: Journal Article
• Authors: Wang, Zi Xiu; He, Q. Peter; Wang, Jin
• Is Part Of: Journal of process control. Volume 26(2015:Feb.)
• Page Start: 56
• Page End: 72
• Publication Date: 2015-02
• Subjects: Variable selection -- Soft sensor -- Partial least squares -- Principal component analysis -- Consistency index -- Information entropy; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2015.01.003
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: Data-driven soft sensors have been widely used in both academic research and industrial applications for predicting hard-to-measure variables or replacing physical sensors to reduce cost. It has been shown that the performance of these data-driven soft sensors could be greatly improved by selecting only the vital variables that strongly affect the primary variables, rather than using all the available process variables. In this work, a comprehensive evaluation of different variable selection methods for PLS-based soft sensor development is presented, and a new metric is proposed to assess the performance of different variable selection methods. The following seven variable selection methods are compared: stepwise regression (SR), partial least squares with regression coefficients (PLS-BETA), PLS with variable importance in projection (PLS-VIP), uninformative variable elimination with PLS (UVE-PLS), genetic algorithm with PLS (GA-PLS), least absolute shrinkage and selection operator (Lasso), and competitive adaptive reweighted sampling with PLS (CARS-PLS). Their strengths and limitations for soft sensor development are demonstrated by a simulated case study and an industrial case study.