Fault Diagnosis by Bayesian Network Classifiers with a Distance Rejection Criterion

被引：1

作者：

Atoui, M. Amine ^{[1
]}

Cohen, Achraf ^{[2
]}

Rauffet, Phillipe ^{[1
]}

Berruet, Pascal ^{[1
]}

机构：

[1] Univ Bretagne Sud, Lab STICC, UMR 6285, CNRS, Lorient, France

[2] Univ West Florida, Dept Math & Stat, Pensacola, FL 32514 USA

来源：

ICINCO: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, VOL 1 | 2019年

关键词：

Bayesian Networks; Classification; Fault Diagnosis; Distance Rejection Criterion;

D O I：

10.5220/0008053304630468

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, Bayesian network classifiers (BNCs) are used as a statistical tool to diagnosis faults with a distance rejection criterion. The proposed approach enhances significantly the structure of the use of Bayesian networks in the same context. Our framework is evaluated and compared to state of the art using data from the benchmark Tennessee Eastman Process (TEP).

引用

页码：463 / 468

页数：6

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