A Kernel-Based Bayesian Classifier for Fault Detection and Classification

被引：0

作者：

Yu, ChunMei ^{[1
]}

Pan, Quan ^{[1
]}

Cheng, YongMei ^{[1
]}

Zhang, HongCai ^{[1
]}

机构：

[1] NW Polytech Univ, Coll Automat, Xian 710072, Shaanxi, Peoples R China

来源：

2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23 | 2008年

关键词：

Kernel Fisher discriminant analysis; Kernel-based Bayesian decision function; Fault detection; Kernel function construction;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：124 / 128

页数：5

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