Exploring Dynamic Belief Networks for Telecommunications Fault Management

Roy Sterritt, AH Marshall, CM Shapcott, Sally McClean

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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
Title of host publicationIEEE SMC 2000 Conference Proceedings
Place of Publicationdoi:10.1109/ICSMC.2000.886576
PublisherIEEE
Pages3646-3652
Number of pages7
VolumeVol. 5
DOIs
Publication statusPublished - Oct 2000
EventIEEE International Conference on Systems, Man and Cybernetics - Nashville, Tennessee, USA
Duration: 1 Oct 2000 → …

Conference

ConferenceIEEE International Conference on Systems, Man and Cybernetics
Period1/10/00 → …

Keywords

  • dynamic bayesian belief networks
  • telecommunication networks
  • fault management
  • intelligent systems

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