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Improved principal component monitoring using the local approach

Lookup NU author(s): Dr Sukhbinder Kumar

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Abstract

This paper shows that current multivariate statistical monitoring technology may not detect incipient changes in the variable covariance structure nor changes in the geometry of the underlying variable decomposition. To overcome these deficiencies, the local approach is incorporated into the multivariate statistical monitoring framework to define two new univariate statistics for fault detection. Fault isolation is achieved by constructing a fault diagnosis chart which reveals changes in the covariance structure resulting from the presence of a fault. A theoretical analysis is presented and the proposed monitoring approach is exemplified using application studies involving recorded data from two complex industrial processes. © 2007 Elsevier Ltd. All rights reserved.

Publication metadata

Author(s): Kruger U, Kumar S, Littler T

Publication type: Article

Publication status: Published

Journal: Automatica

Year: 2007

Volume: 43

Issue: 9

Pages: 1532-1542

ISSN (print): 0005-1098

ISSN (electronic): 1873-2836

Publisher: Pergamon

URL: http://dx.doi.org/10.1016/j.automatica.2007.02.016

DOI: 10.1016/j.automatica.2007.02.016

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