Fault reconstruction algorithm based on fault-relevant KPCA

Author

Zhang Yingwei ; Wang Zhengbing

Author_Institution

State Lab. of Synthesis Autom. of Process Ind., Northeastern Univ., Shenyang, China

fYear

2013

fDate

25-27 May 2013

Firstpage

4319

Lastpage

4323

Abstract

In this paper, a fault reconstruction algorithm based on fault-relevant KPCA is proposed. Compared with the traditional fault reconstruction method whose fault model is composed of the first major distribution directions, the proposed reconstruction algorithm gives a deep analysis of the original fault space according to the relationships with normal process information to extract the principal directions that are relevant to, or affected by fault. Considering the nonlinear situation, kernel PCA is applied. Simulation results on the penicillin fermentation process demonstrate the effectiveness of the proposed algorithm.

Keywords

drugs; fault diagnosis; fermentation; principal component analysis; process monitoring; deep analysis; fault model; fault reconstruction algorithm; fault space; fault-relevant KPCA; kernel principal component analysis; major distribution directions; nonlinear situation; normal process information; penicillin fermentation process; principal directions extract; Decision support systems; TV; Kernel principal component analysis (KPCA); fault-relevant KPCA; fault-relevant directions; subspace;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2013 25th Chinese

Conference_Location

Guiyang

Print_ISBN

978-1-4673-5533-9

Type

conf

DOI

10.1109/CCDC.2013.6561711

Filename

6561711