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A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring. Processes (Basel) 2019. [DOI: 10.3390/pr8010024] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
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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Chang P, Kang O, Chunhao D, Lu R. Application of fault monitoring and diagnosis in process industry based on fourth order moment and singular value decomposition. CAN J CHEM ENG 2019. [DOI: 10.1002/cjce.23670] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Peng Chang
- Faculty of Information and TechnologyBeijing University of Technology Beijing China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China
| | - Olivia Kang
- Faculty of Information and TechnologyBeijing University of Technology Beijing China
| | - Ding Chunhao
- Faculty of Information and TechnologyBeijing University of Technology Beijing China
| | - Ruiwei Lu
- Faculty of Information and TechnologyBeijing University of Technology Beijing China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China
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Wang Y, Zhou D. Preface of the fault detection, supervision and safety for chemical processes. CAN J CHEM ENG 2018. [DOI: 10.1002/cjce.23087] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Youqing Wang
- College of Electrical Engineering and Automation; Shandong University of Science and Technology; Qingdao China
| | - Donghua Zhou
- College of Electrical Engineering and Automation; Shandong University of Science and Technology; Qingdao China
- Department of Automation; Tsinghua University; Beijing China
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