Quantitative Analysis of Dynamic Fault Trees Based on the Structure Function

descriptionPublicationkeyboard_double_arrow_right Article 29 Jan 2013 English Publisher:WileyJournal:Quality and Reliability Engineering International, volume 30, pages 143-156 (issn: 0748-8017, eissn: 1099-1638,

Copyright policy )

Authors: Guillaume Merle; Jean-Marc Roussel; Jean-Jacques Lesage;

doi: 10.1002/qre.1487

Quantitative Analysis of Dynamic Fault Trees Based on the Structure Function

- Summary
- Subjects
- Metrics

Abstract

This paper presents a probabilistic model of dynamic gates which allows to perform the quantitative analysis of any dynamic fault tree (DFT) from its structure function. Both these probabilistic models and the quantitative analysis which can be performed thanks to them can accommodate any failure distribution of basic events. We illustrate our approach on a DFT example from the literature. Copyright © 2013 John Wiley & Sons, Ltd.

Related Organizations

University of Paris-Saclay
France
LABORATOIRE UNIVERSITAIRE DE RECHERCHE EN PRODUCTION AUTOMATISEE
France
École normale supérieure de Cachan
France

Keywords

probabilistic model, [SPI.AUTO] Engineering Sciences [physics]/Automatic, quantitative analysis, Dynamic fault tree, structure function, quantitative analysis.

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	53
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%