Criteria for optimizing kernel methods in fault monitoring process: A survey

被引：10

作者：

Bernal-de-Lazaro, Jose M. ^{[1
]}

-Corona, Carlos Cruz ^{[2
]}

Silva-Neto, Antonio J. ^{[3
]}

Llanes-Santiago, Orestes ^{[1
]}

机构：

[1] Univ Tecnol Habana Jose Antonio Echeverria, CUJAE, Dept Automat & Comp, Havana, Cuba

[2] Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada, Spain

[3] Univ Estado Rio De Janeiro, IPRJ UERJ, Dept Mech Engn, Rio de Janeiro, RJ, Brazil

来源：

ISA TRANSACTIONS | 2022年 / 127卷

关键词：

Kernel methods; Kernel functions; Fault detection; Data preprocessing; Kernel parameter; Objective functions; INDEPENDENT COMPONENT ANALYSIS; SUPPORT VECTOR MACHINE; FISHER DISCRIMINANT-ANALYSIS; PARTICLE SWARM OPTIMIZATION; CANONICAL VARIATE ANALYSIS; FEATURE-EXTRACTION; NONLINEAR PROCESSES; DATA-DRIVEN; DIAGNOSIS; KPCA;

D O I：

10.1016/j.isatra.2021.08.040

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Nowadays, how to select the kernel function and their parameters for ensuring high-performance indicators in fault diagnosis applications remains as two open research issues. This paper provides a comprehensive literature survey of kernel-preprocessing methods in condition monitoring tasks, with emphasis on the procedures for selecting their parameters. Accordingly, twenty kernel optimization criteria and sixteen kernel functions are analyzed. A kernel evaluation framework is further provided for helping in the selection and adjustment of kernel functions. The proposal is validated via a KPCA-based monitoring scheme and two well-known benchmark processes. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

引用

页码：259 / 272

页数：14

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