Dynamic principal component analysis using subspace model identification

Authors:
Pingkang Li;Richard J. Treasure;Uwe Kruger
Affiliations:
School of Mechanical, Electrical and Control Engineering, Beijing Jiaotong University, Beijing, P.R. China;Control Systems Research Group, University of Western Australia, Crawley;Intelligent Systems and Control Group, Queen's University, Belfast, UK
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

Citing 2
Cited 2

N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems

Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
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Improved principal component monitoring using the local approach

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Fault detection in multivariate signals with applications to gas turbines

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Abstract

This work analyses a recently proposed statistically based technique for monitoring complex dynamic process systems [17]. The technique utilises a state space model that is cast into the multivariate statistical process control framework (i) to define a set of state variables that can describe dynamic process behaviour, (ii) to generate univariate statistics that can monitor dynamic process behaviour and (iii) to construct contribution plots from these statistics that can diagnose anomalous process behaviour. The presented analysis reveals that the size of the state space monitoring model can be reduced. The utility of the improved dynamic monitoring technique is demonstrated using an industrial application study to a glass-melter process.