Bayesian Networks for Reliability Analysis of Partially Observed Systems

Bayan Hamdan, Pingfeng Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Due to the observability limitations imposed by large-scale systems, it becomes vital to introduce methods to understand the state of the system and its components to aid in their maintainability. This study presents a Bayesian Network based approach to mapping out the system and component states of complex systems. Specifically, the Holdup Tank case study is considered to represent the case in which there is a dynamic dependence between system components. Moreover, in complex systems, learning the parameters and structure of the model can be extremely costly. This study incorporates the use of constraint graphs to reduce the variables in the model and improve the learning accuracy of the parameters as well as structure of the Bayesian Network Model. Accounting for the constraint graph allows decision makers to make more accurate inferences on the system and component states with less data. The results show an improvement to the learned model given a relatively small dataset.

Original languageEnglish (US)
Title of host publication67th Annual Reliability and Maintainability Symposium, RAMS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728180175
DOIs
StatePublished - 2021
Externally publishedYes
Event67th Annual Reliability and Maintainability Symposium, RAMS 2021 - Orlando, United States
Duration: May 24 2021May 27 2021

Publication series

NameProceedings - Annual Reliability and Maintainability Symposium
Volume2021-May
ISSN (Print)0149-144X

Conference

Conference67th Annual Reliability and Maintainability Symposium, RAMS 2021
Country/TerritoryUnited States
CityOrlando
Period5/24/215/27/21

Keywords

  • RAMS Proceedings
  • final paper preparation
  • format instructions

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Mathematics(all)
  • Computer Science Applications

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