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A sparse PCA for nonlinear fault diagnosis and robust feature discovery of industrial processes

Citation

Yu, H and Khan, F and Garaniya, V, A sparse PCA for nonlinear fault diagnosis and robust feature discovery of industrial processes, AIChE Journal, 62, (5) pp. 1494-1513. ISSN 1547-5905 (2016) [Refereed Article]

Copyright Statement

Copyright 2016 American Institute of Chemical Engineers

DOI: doi:10.1002/aic.15136

Abstract

Pearson's correlation measure is only able to model linear dependence between random variables. Hence, conventional principal component analysis (PCA) based on Pearson's correlation measure is not suitable for application to modern industrial processes where process variables are often nonlinearly related. To address this problem, a nonparametric PCA model is proposed based on nonlinear correlation measures, including Spearman's and Kendall tau's rank correlation. These two correlation measures are also less sensitive to outliers comparing to Pearson's correlation, making the proposed PCA a robust feature extraction technique. To reveal meaningful patterns from process data, a generalized iterative deflation method is applied to the robust correlation matrix of the process data to sequentially extract a set of leading sparse pseudoeigenvectors. For online fault diagnosis, the T2 and SPE statistics are computed and analyzed with respect to the subspace spanned by the extracted pseudoeigenvectors. The proposed method is applied to two industrial case studies. Its process monitoring performance is demonstrated to be superior to that of the conventional PCA and is comparable to those of Kernel PCA and kernel independent component analysis at a lower computational cost. The proposed PCA is also more robust in sparse feature extraction from contaminated process data.

Item Details

Item Type:Refereed Article
Keywords:principal component analysis, Spearmanís rank correlation, Kendall tauís rank correlation, nonlinear process monitoring, robust feature discovery
Research Division:Engineering
Research Group:Chemical Engineering
Research Field:Process Control and Simulation
Objective Division:Energy
Objective Group:Environmentally Sustainable Energy Activities
Objective Field:Environmentally Sustainable Energy Activities not elsewhere classified
Author:Yu, H (Mr Hongyang Yu)
Author:Khan, F (Professor Faisal Khan)
Author:Garaniya, V (Dr Vikram Garaniya)
ID Code:108250
Year Published:2016
Web of Science® Times Cited:9
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2016-04-15
Last Modified:2017-11-03
Downloads:0

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