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Fault detection of dynamic processes using a simplified monitoring-specific CVA state space approach

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.

Publication metadata

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


DOI: 10.1016/S1570-7946(09)70057-4

Notes: 19th European Symposium on Computer Aided Process Engineering


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