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An alternative formulation of PCA for process monitoring using distance correlation


Yu, H and Khan, F and Garaniya, V, An alternative formulation of PCA for process monitoring using distance correlation, Industrial and Engineering Chemistry Research, 55, (3) pp. 656-669. ISSN 0888-5885 (2016) [Refereed Article]

Copyright Statement

Copyright 2015 American Chemical Society

DOI: doi:10.1021/acs.iecr.5b03397


Scale-invariant principal component analysis (PCA) is prevalent in process monitoring because of its simplicity and efficiency. However, a number of limitations are associated with this technique because of underlying assumptions. This article attempts to relax these limitations by introducing three key elements. First, a semiparametric Gaussian transformation is proposed to make the process data follow a multivariate Gaussian distribution, such that the standard PCA can be directly applied to explain the majority of the process data variance. The Gaussian transformation function preserves both important statistical information and the correlation structures of the process data. Second, eigenvectors spanning the feature space are extracted using the Spearman correlation coefficient and the distance correlation coefficient. This feature space is able to retain nonlinear and nonmonotonic correlation structures of the process data. Finally, this technique is computationally more efficient than KPCA, KICA, and improved KICA by avoiding expensive kernel mapping. Semiparametric PCA is tested on two industrial case studies and exhibits satisfactory performance.

Item Details

Item Type:Refereed Article
Keywords:principal component analysis, semi-parametric Gaussian transformation, Spearmanís correlation coefficient, distance correlation coefficient, process monitoring
Research Division:Engineering
Research Group:Chemical engineering
Research Field:Process control and simulation
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in engineering
UTAS Author:Yu, H (Mr Hongyang Yu)
UTAS Author:Khan, F (Professor Faisal Khan)
UTAS Author:Garaniya, V (Dr Vikram Garaniya)
ID Code:106760
Year Published:2016
Web of Science® Times Cited:23
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2016-02-19
Last Modified:2017-11-03

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