A review of kernel methods for feature extraction in nonlinear process monitoring

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dc.contributor.authorPilario, Karl Ezra
dc.contributor.authorShafiee, Mahmood
dc.contributor.authorCao, Yi
dc.contributor.authorLao, Liyun
dc.contributor.authorYang, Shuang-Hua
dc.date.accessioned2020-01-02T16:29:48Z
dc.date.available2020-01-02T16:29:48Z
dc.date.issued2019-12-23
dc.description.abstractKernel 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.en_UK
dc.identifier.citationPilario KE, Shafiee M, Cao Y, et al., (2019) A review of kernel methods for feature extraction in nonlinear process monitoring. Processes, Volume 8, Issue 1, December 2019, Article number 24en_UK
dc.identifier.issn2227-9717
dc.identifier.urihttps://doi.org/10.3390/pr8010024
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/14878
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectkernel PCAen_UK
dc.subjectkernel PLSen_UK
dc.subjectkernel ICAen_UK
dc.subjectkernel CCAen_UK
dc.subjectkernel FDAen_UK
dc.subjectmultivariate statisticen_UK
dc.subjectfault detectionen_UK
dc.subjectmachine learningen_UK
dc.titleA review of kernel methods for feature extraction in nonlinear process monitoringen_UK
dc.typeArticleen_UK

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