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Nonlinear dynamic process monitoring based on dynamic kernel principal component analysis (DKPCA) is proposed. The kernel functions used in kernel PCA (KPCA) are profitable for capturing nonlinear property of processes and the time-lagged data extension is suitable for describing dynamic characteristic of processes. DKPCA enables us to monitor an arbitrary process with severe nonlinearity and (or) dynamics. In this respect, it is a generalized concept of multivariate statistical monitoring approaches. A unified monitoring index combined T with SPE is also suggested. The proposed monitoring method based on DKPCA is applied to a simulated nonlinear process and a wastewater treatment process. A comparison study of PCA, dynamic PCA, KPCA, and DKPCA is investigated in terms of type I error rate, type II error rate, and detection delay. The monitoring results confirm that the proposed methodology results in the best monitoring performance, i.e., low missing alarms and small detection delay, for all the faults. © 2004 Elsevier Ltd. All rights reserved.
Author(s): Choi SW, Lee I-B
Publication type: Article
Publication status: Published
Journal: Chemical Engineering Science
Year: 2004
Volume: 59
Issue: 24
Pages: 5897-5908
ISSN (print): 0009-2509
ISSN (electronic): 1873-4405
Publisher: Pergamon
URL: http://dx.doi.org/10.1016/j.ces.2004.07.019
DOI: 10.1016/j.ces.2004.07.019
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