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Modified canonical variate analysis based on dynamic kernel decomposition for dynamic nonlinear process quality monitoring.

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Title: Modified canonical variate analysis based on dynamic kernel decomposition for dynamic nonlinear process quality monitoring.
Authors: Zhang, Ming-Qing1 (AUTHOR) zmq_charles@126.com, Luo, Xiong-Lin1 (AUTHOR) luoxl@cup.edu.cn
Source: ISA Transactions. Feb2021, Vol. 108, p106-120. 15p.
Subject Terms: Kernel (Mathematics), Singular value decomposition, Kernel functions, Mathematical decomposition, Orthogonal decompositions, Latent variables, Product quality, Product improvement
Abstract: It is crucial to adopt an efficient process monitoring technique that ensures process operation safety and improves product quality. Toward this endeavor, a modified canonical variate analysis based on dynamic kernel decomposition (DKDCVA) approach is proposed for dynamic nonlinear process quality monitoring. Different from traditional canonical variate analysis and its expansive kernel methods, the chief intention of the our proposed method is to establish a partial-correlation nonlinear model between input dynamic kernel latent variables and output variables, and ensures the extracted feature information can be maximized. More specifically, the dynamic nonlinear model is orthogonally decomposed to obtain quality-related and independent subspace by singular value decomposition. From the perspective of quality monitoring, Hankel matrices of past and future vectors of quality-related subspace are derived in detail, and corresponding statistical metrics are constructed. Furthermore, given the existence of non-Gaussian process variables, kernel density estimation evaluates the upper control limit instead of traditional control limits. Finally, the experimental results conducted on a simple numerical example, the Tennessee Eastman process and the hot strip mill process indicate that the DKDCVA approach can be preferable to monitor abnormal operation for the dynamic nonlinear process. • Modified canonical variate analysis is present on dynamic kernel decomposition. • Dynamic partial nonlinear model is established for input-output variables. • Quality-related feature information is obtained by orthogonal decomposition. • Hankel matrices of past and future vectors are deriving for quality monitoring. [ABSTRACT FROM AUTHOR]
Database: Supplemental Index
Description
Abstract:It is crucial to adopt an efficient process monitoring technique that ensures process operation safety and improves product quality. Toward this endeavor, a modified canonical variate analysis based on dynamic kernel decomposition (DKDCVA) approach is proposed for dynamic nonlinear process quality monitoring. Different from traditional canonical variate analysis and its expansive kernel methods, the chief intention of the our proposed method is to establish a partial-correlation nonlinear model between input dynamic kernel latent variables and output variables, and ensures the extracted feature information can be maximized. More specifically, the dynamic nonlinear model is orthogonally decomposed to obtain quality-related and independent subspace by singular value decomposition. From the perspective of quality monitoring, Hankel matrices of past and future vectors of quality-related subspace are derived in detail, and corresponding statistical metrics are constructed. Furthermore, given the existence of non-Gaussian process variables, kernel density estimation evaluates the upper control limit instead of traditional control limits. Finally, the experimental results conducted on a simple numerical example, the Tennessee Eastman process and the hot strip mill process indicate that the DKDCVA approach can be preferable to monitor abnormal operation for the dynamic nonlinear process. • Modified canonical variate analysis is present on dynamic kernel decomposition. • Dynamic partial nonlinear model is established for input-output variables. • Quality-related feature information is obtained by orthogonal decomposition. • Hankel matrices of past and future vectors are deriving for quality monitoring. [ABSTRACT FROM AUTHOR]
ISSN:00190578
DOI:10.1016/j.isatra.2020.08.017