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A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

Pilario, K., Shafiee, M., Cao, Y., Lao, L., Yang, S.-H. (2020) A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring. Processes, 8 (1). Article Number 24. ISSN 2227-9717. E-ISSN 2227-9717. (doi:10.3390/pr8010024) (KAR id:79671)

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Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries.

Item Type: Article
DOI/Identification number: 10.3390/pr8010024
Uncontrolled keywords: kernel PCA; kernel PLS; kernel ICA; kernel CCA; kernel CVA; kernel FDA; multivariate statistics; fault detection; fault diagnosis; machine learning
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation
T Technology > TJ Mechanical engineering and machinery
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Mahmood Shafiee
Date Deposited: 22 Jan 2020 18:21 UTC
Last Modified: 16 Feb 2021 14:10 UTC
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