期刊
PROCESSES
卷 8, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/pr8010024
关键词
kernel PCA; kernel PLS; kernel ICA; kernel CCA; kernel CVA; kernel FDA; multivariate statistics; fault detection; fault diagnosis; machine learning
资金
- Faculty Development Fund of the Engineering Research and Development for Technology (ERDT) program of the Department of Science and Technology (DOST), Philippines
- National Key Research and Development Plan of P. R. China [2018YFC0214102]
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.
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