Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis

Christophe Simon (1) , Philippe Weber (1) , Alexandre Evsukoff (2)

1 CRAN - Centre de Recherche en Automatique de Nancy
2 COPPE-UFRJ - Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia

This paper deals with the use of Bayesian networks to compute system reliability. The reliability analysis problem is described and the usual methods for quantitative reliability analysis are presented within a case study. Some drawbacks that justify the use of Bayesian networks are identified. The basic concepts of the Bayesian networks application to reliability analysis are introduced and a model to compute the reliability for the case study is presented. Dempster Shafer theory to treat epistemic uncertainty in reliability analysis is then discussed and its basic concepts that can be applied thanks to the Bayesian network inference algorithm are introduced. Finally, it is shown, with a numerical example, how Bayesian networks' inference algorithms compute complex system reliability and what the Dempster Shafer theory can provide to reliability analysis.

Mots clés: Reliability; Epistemic Uncertainty; Dempster Shafer Theory; Bayesian Networks; Evidence Networks

Domaines: Automatique

Christophe Simon, Philippe Weber, Alexandre Evsukoff. Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis. Reliability Engineering and System Safety, 2008, 93 (7), pp.950-963. ⟨10.1016/j.ress.2007.03.012⟩. ⟨hal-00139492⟩