Skip to Main content Skip to Navigation
Conference papers

Modélisation des causes communes de défaillance d'un système instrumenté de sécurité particulier

Abstract : Common cause failures (CCFs) are an important part of reliability analysis when working with safety instrumented systems (SIS), and engineers have been aware of these types of failures since the midseventies [1]. The purpose of this article is to develop a strategy to study an example on oil-pressure system and propose a CCF-strategy for that present example. The focus is given to the following three methods: the beta-factor model, the PDS method [2], and Markov analysis with stochastic simulation. The need for Markov analysis becomes evident when working with SIS of a more complex nature, for instance non-identical components. Finally, it is always important to remember that if there exists any feedback data or expert knowledge describing the distribution of the number of components that fail in a CCF, this is vital in deciding the most descriptive CCF model. By the term descriptive model, we mean a model that both describes the architecture of the system as accurately as possible, and also makes as few assumptions as possible.
Document type :
Conference papers
Complete list of metadatas

Cited literature [4 references]  Display  Hide  Download

https://hal-ineris.archives-ouvertes.fr/ineris-00973332
Contributor : Gestionnaire Civs <>
Submitted on : Friday, April 4, 2014 - 10:21:59 AM
Last modification on : Friday, April 4, 2014 - 10:21:59 AM
Long-term archiving on: : Friday, July 4, 2014 - 11:20:20 AM

File

2009-058_hal.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : ineris-00973332, version 1
  • INERIS : EN-2009-058

Collections

Citation

Torbjorn Lilleheier, Florent Brissaud. Modélisation des causes communes de défaillance d'un système instrumenté de sécurité particulier. 8. Congrès International pluridisciplinaire QUALITA 2009, Mar 2009, Besançon, France. pp.NC. ⟨ineris-00973332⟩

Share

Metrics

Record views

314

Files downloads

2111