Failure importance analysis and adjustment based on Bayesian networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Importance measures in reliability engineering are used to find the weak areas of a system. Traditional importance measures for binary systems and multi-state systems mainly concern reliability importance of an individual component, and seldom consider the reliability importance of the causal components. This paper constructs the failure importance analysis Bayesian networks (FIABN) to describe the causality system firstly. Then we present the failure importance measures models for binary and multi-state systems based on FIABN. Finally, the adjustment methods of the failure importance are given. The numerical simulations show failure importance measures models and adjustment methods are effective.

Original languageEnglish
Title of host publication2nd International Symposium on Information Science and Engineering, ISISE 2009
PublisherIEEE Computer Society
Pages303-308
Number of pages6
ISBN (Print)9780769539911
DOIs
StatePublished - 2009
Event2009 2nd International Symposium on Information Science and Engineering, ISISE 2009 - Shanghai, China
Duration: 26 Dec 200928 Dec 2009

Publication series

Name2nd International Symposium on Information Science and Engineering, ISISE 2009

Conference

Conference2009 2nd International Symposium on Information Science and Engineering, ISISE 2009
Country/TerritoryChina
CityShanghai
Period26/12/0928/12/09

Keywords

  • Bayesian networks
  • Causal system
  • Failure importance adjustment
  • Failure importance measure
  • Model

Fingerprint

Dive into the research topics of 'Failure importance analysis and adjustment based on Bayesian networks'. Together they form a unique fingerprint.

Cite this