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Since many of the current multivariate statistical process monitoring (MSPM) techniques are based on the assumptions that the process has one nominal operating region, the application of these MSPM approaches to an industrial process with multiple operating modes would always trigger continuous warnings even when the process itself is operating under another steady operating condition. Adopting new metrics in the form of principal angles to measure the similarity of any two models, this paper proposes a multiple PCA (principal component analysis) model based process monitoring method. In addition, median-filter is utilized to adapt both the possible set-point changes and slow process variations. Some popular multivariate statistic such as SPE and its control limit can be incorporated straightforwardly to facilitate the process monitoring. The efficiency of the proposed technique is demonstrated by application to an industrial fluidized catalytic cracking (FCC) unit and the proposed scheme can reduce the amount of false alarms to a great extent and simultaneously track the process adjustment. © 2004 Elsevier B.V. All rights reserved.
Author(s): Zhao S, Xu Y, Zhang J
Publication type: Article
Publication status: Published
Journal: Computer Aided Chemical Engineering
Year: 2004
Volume: 18
Issue: C
Pages: 865-870
ISSN (print): 1570-7946
ISSN (electronic):
Publisher: Elsevier BV
URL: http://dx.doi.org/10.1016/S1570-7946(04)80210-4
DOI: 10.1016/S1570-7946(04)80210-4
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