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A Deep Residual PLS for Data-Driven Quality Prediction Modeling in Industrial Process
Xiaofeng Yuan; Weiwei Xu; Yalin Wang; Chunhua Yang; Weihua Gui
发表期刊IEEE/CAA Journal of Automatica Sinica
ISSN2329-9266
2024
卷号11期号:8页码:1777-1785
摘要Partial least squares (PLS) model is the most typical data-driven method for quality-related industrial tasks like soft sensor. However, only linear relations are captured between the input and output data in the PLS. It is difficult to obtain the remaining nonlinear information in the residual subspaces, which may deteriorate the prediction performance in complex industrial processes. To fully utilize data information in PLS residual subspaces, a deep residual PLS (DRPLS) framework is proposed for quality prediction in this paper. Inspired by deep learning, DRPLS is designed by stacking a number of PLSs successively, in which the input residuals of the previous PLS are used as the layer connection. To enhance representation, nonlinear function is applied to the input residuals before using them for stacking high-level PLS. For each PLS, the output parts are just the output residuals from its previous PLS. Finally, the output prediction is obtained by adding the results of each PLS. The effectiveness of the proposed DRPLS is validated on an industrial hydrocracking process.
关键词Deep residual partial least squares (DRPLS) nonlinear function quality prediction soft sensor
DOI10.1109/JAS.2024.124578
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文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/58547
专题学术期刊_IEEE/CAA Journal of Automatica Sinica
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Xiaofeng Yuan,Weiwei Xu,Yalin Wang,et al. A Deep Residual PLS for Data-Driven Quality Prediction Modeling in Industrial Process[J]. IEEE/CAA Journal of Automatica Sinica,2024,11(8):1777-1785.
APA Xiaofeng Yuan,Weiwei Xu,Yalin Wang,Chunhua Yang,&Weihua Gui.(2024).A Deep Residual PLS for Data-Driven Quality Prediction Modeling in Industrial Process.IEEE/CAA Journal of Automatica Sinica,11(8),1777-1785.
MLA Xiaofeng Yuan,et al."A Deep Residual PLS for Data-Driven Quality Prediction Modeling in Industrial Process".IEEE/CAA Journal of Automatica Sinica 11.8(2024):1777-1785.
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