Sensor fault diagnosis of nonlinear processes based on structured kernel principal component analysis

被引：19

作者：

Fu K. ^{[1
,2
]}

Dai L. ^{[2
]}

Wu T. ^{[2
]}

Zhu M. ^{[1
]}

机构：

[1] Department of Control Engineering, Chengdu University of Information Technology

[2] National Key Laboratory of Industrial Control Technology, Zhejiang University

来源：

Journal of Control Theory and Applications | 2009年 / 7卷 / 3期

关键词：

Incidence matrix optimization; Sensor fault diagnosis; Structured KPCA;

D O I：

10.1007/s11768-009-7164-9

中图分类号：

学科分类号：

摘要：

A new sensor fault diagnosis method based on structured kernel principal component analysis (KPCA) is proposed for nonlinear processes. By performing KPCA on subsets of variables, a set of structured residuals, i.e., scaled powers of KPCA, can be obtained in the same way as partial PCA. The structured residuals are utilized in composing an isolation scheme for sensor fault diagnosis, according to a properly designed incidence matrix. Sensor fault sensitivity and critical sensitivity are defined, based on which an incidence matrix optimization algorithm is proposed to improve the performance of the structured KPCA. The effectiveness of the proposed method is demonstrated on the simulated continuous stirred tank reactor (CSTR) process. © South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag GmbH 2009.

引用

页码：264 / 270

页数：6

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