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Lookup NU author(s): Shallon Stubbs, Dr Jie ZhangORCiD, Emeritus Professor Julian Morris
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There have been much reported successes with the use of state space models for process identification, control and monitoring of dynamic processes with several different approaches to deriving the state variables and a few variants of the state space model representation having been documented over the years. Typically the form of statespace model adopted is one that requires the estimation of five matrices to fully parameterize the model. This paper proposes a simplification of the state-space model for the specific purpose of process monitoring. The simplification of the representation achieved via a modified definition of the past vector of inputs and output, facilitates a simpler and more efficiently estimation of a reduced set of state space matrices. The proposed approach in conjunction with a devised filtering method gives improved fault detection performance over the established state space monitoring methods and other multivariate statistical methods. © 2009 Elsevier B.V. All rights reserved.
Author(s): Stubbs S, Zhang J, Morris J
Publication type: Article
Publication status: Published
Journal: Computer Aided Chemical Engineering
Year: 2009
Volume: 26
Pages: 339-344
ISSN (print): 1570-7946
ISSN (electronic):
Publisher: Elsevier BV
URL: http://dx.doi.org/10.1016/S1570-7946(09)70057-4
DOI: 10.1016/S1570-7946(09)70057-4
Notes: 19th European Symposium on Computer Aided Process Engineering
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