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A Principal Component Approach in Diagnosing poor Control loop performance

H., Zabiri and T.D.T. , Thao (2007) A Principal Component Approach in Diagnosing poor Control loop performance. In: Proceedings of the World Congress on Engineering and Computer Science 2007, October 24-26, 2007, San Francisco, USA.

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Abstract

Principal component analysis, both linear and nonlinear, are used to identify and remove correlations among process variables as an aid to dimensionality reduction, visualization, and exploratory data analysis. While PCA ascertains only linear correlations between variables, NLPCA reveals both linear and nonlinear correlations, without restriction on the character of the nonlinearities present in the data. In this paper, the use of PCA and NLPCA are investigated and compared for nonlinearity detection in regulated systems using routine operating data. Results from simulated and industrial data used in this study clearly show that NLPCA performance supersedes that of PCA in identifying and detecting nonlinearity in poor performing control loops.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Poor control loop diagnosis, PCA, NLPCA
Subjects:T Technology > TP Chemical technology
Departments / MOR / COE:Departments > Chemical Engineering
ID Code:3746
Deposited By: Haslinda Zabiri
Deposited On:28 Dec 2010 07:04
Last Modified:19 Jan 2017 08:27

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