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

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 metho...

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Bibliographic Details
Published in:Processes 2020-01, Vol.8 (1), p.24
Main Authors: Pilario, Karl Ezra, Shafiee, Mahmood, Cao, Yi, Lao, Liyun, Yang, Shuang-Hua
Format: Article
Language:English
Subjects:
Artificial intelligence
Data analysis
Datasets
Fault diagnosis
Feature extraction
Industrial plants
Kernel functions
Knowledge
Learning algorithms
Machine learning
Monitoring
Physics
Principal components analysis
Route selection
Variables
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Summary: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.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr8010024