Exploring dynamic Bayesian Belief Networks for intelligent fault management systems

R. Sterritt*, A. H. Marshall, C. M. Shapcott, S. I. McClean

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

27 Citations (Scopus)

Abstract

Systems that are subject to uncertainty in their behaviour are often modelled by Bayesian Belief Networks (BBNs). These are probabilistic models of the system in which the independence relations between the variables of interest are represented explicitly. A directed graph is used in which two nodes are connected by an edge if one is a `direct cause' of the other. However the Bayesian paradigm does not provide any direct means for modelling dynamic systems. There has been a considerable amount of research effort in recent years to address this. In this paper, we review these approaches and propose a new dynamic extension to the BBN. Our discussion then focuses on fault management of complex telecommunications and how the dynamic bayesian models can assist in the prediction of faults.

Original languageEnglish
Pages3646-3652
Number of pages7
Publication statusPublished - 01 Dec 2000
Externally publishedYes
Event2000 IEEE International Conference on Systems, Man and Cybernetics - Nashville, TN, USA
Duration: 08 Oct 200011 Oct 2000

Conference

Conference2000 IEEE International Conference on Systems, Man and Cybernetics
CityNashville, TN, USA
Period08/10/200011/10/2000

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

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