Title :
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
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;
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
DOI :
10.1109/WCICA.2008.4592910
