A kernel-based bayesian classifier for fault detection and classification

Author

Yu, ChunMei ; Pan, Quan ; Cheng, Yongmei ; Zhang, Hongcai

Author_Institution

Coll. of Autom., Northwestern Polytech. Univ., Xian

fYear

2008

fDate

25-27 June 2008

Firstpage

124

Lastpage

128

Abstract

A kernel constructed by Shannon sampling function was utilized for kernel Fisher discriminant analysis (KFDA). And kernel-based Bayesian decision function was implemented for fault detection. Simultaneously, Bhattacharyya distance was introduced as a criterion function for separability comparison. The proposed Shannon KFDA was compared with Gaussian KFDA on Tennessee Eastman Process (TEP) data. The results show that Shannon KFDA has lager Bhattacharyya distance and detects more faults more quickly than Gaussian KFDA.

Keywords

Bayes methods; pattern classification; sampling methods; Bhattacharyya distance; Shannon sampling function; fault detection; kernel Fisher discriminant analysis; kernel-based Bayesian classifier; kernel-based Bayesian decision function; Automation; Bayesian methods; Fault detection; Gaussian distribution; Intelligent control; Kernel; Sampling methods; Scattering; Support vector machine classification; Support vector machines; Bayesian decision function; Fault detection; Kernel Fisher discriminant analysis; Kernel function construction; Kernel-based;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on

Conference_Location

Chongqing

Print_ISBN

978-1-4244-2113-8

Electronic_ISBN

978-1-4244-2114-5

Type

conf

DOI

10.1109/WCICA.2008.4592910

Filename

4592910