File(s) under permanent embargo
An alternative formulation of PCA for process monitoring using distance correlation
journal contribution
posted on 2023-05-18, 17:13 authored by Yu, H, Faisal KhanFaisal Khan, Vikrambhai GaraniyaVikrambhai GaraniyaScale-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.
History
Publication title
Industrial and Engineering Chemistry ResearchVolume
55Pagination
656-669ISSN
0888-5885Department/School
Australian Maritime CollegePublisher
Amer Chemical SocPlace of publication
1155 16Th St, Nw, Washington, USA, Dc, 20036Rights statement
Copyright 2015 American Chemical SocietyRepository Status
- Restricted