Distributed Diagnosis in Uncertain Environments Using Dynamic Bayesian Networks

被引：2

作者：

Roychoudhury, Indranil ^{[1
]}

Biswas, Gautam ^{[1
]}

Koutsoukos, Xenofon ^{[1
]}

机构：

[1] Vanderbilt Univ, Inst Software Integrated Syst, Nashville, TN 37235 USA

来源：

18TH MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION | 2010年

关键词：

SYSTEMS;

D O I：

10.1109/MED.2010.5547832

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Model-based diagnosis for industrial applications have to be efficient, and deal with modeling approximations and measurement noise. This paper presents a distributed diagnosis scheme, based on Dynamic Bayesian Networks (DBNs) that generates globally correct diagnosis results through local analysis, by only communicating a minimal number of measurements among diagnosers. We demonstrate experimentally that our distributed diagnosis scheme is computationally more efficient than its centralized counterpart, and it does not compromise the accuracy of the diagnosis results.

引用

页码：1531 / 1536

页数：6

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