Improved CPLS Algorithm and Its Application in Process Monitoring

Authors

Qinghua Li, Key Laboratory of Advanced Process Control for Light Industry Ministry of Education, Jiangnan University, Wuxi 214122, China;
Pan Feng, Key Laboratory of Advanced Process Control for Light Industry Ministry of Education, Jiangnan University, Wuxi 214122, China;
Zhonggai Zhao, Key Laboratory of Advanced Process Control for Light Industry Ministry of Education, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: Concurrent PLS (CPLS) further extracts information from the residuals of input variables and quality variables drawn by PLS, thus the raw data are projected into five subspaces. The process monitoring based CPLS provides a whole framework for the monitoring of input variables and quality variables. The model for residuals is developed by a deterministic manner while the residuals are inherently stochastic; therefore a probabilistic model is more proper for describing their features. This paper introduces factor analysis (FA) into CPLS, in which FA instead of PCA is used to analyze the residuals to develop the improved CPLS model, and the monitoring indices for checking the validity of variables satisfying Gaussian distribution are built to improve the consistence between the modeling objective and the monitoring indices.

Recommended Citation

Li, Qinghua; Feng, Pan; and Zhao, Zhonggai (2019) "Improved CPLS Algorithm and Its Application in Process Monitoring," Journal of System Simulation: Vol. 30: Iss. 2, Article 31.
DOI: 10.16182/j.issn1004731x.joss.201802031
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss2/31

First Page

622

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201802031

Last Page

628

CLC

TP391.9

Recommended Citation

Li Qinghua, Pan Feng, Zhao Zhonggai. Improved CPLS Algorithm and Its Application in Process Monitoring[J]. Journal of System Simulation, 2018, 30(2): 622-628.

DOI

10.16182/j.issn1004731x.joss.201802031