Dynamic bayesian networks for modeling advanced fault tree features in dependability analysis

Fault Trees (FT) are one of the most popular techniques for dependability analysis of large, safety critical systems. It has been shown (Bobbio 2001) that FT can be directly mapped into Bayesian Net-works (BN) and that the basic inference techniques on the latter may be used to obtain classical parameters computed from the former. In this paper, we show how BN can provide a unified framework in which also Dynamic FT (DFT), a recent extensions able to treat complex types of dependencies, can be represented. In particular, we propose to characterize dynamic gates within the Dynamic Bayesian Network framework (DBN), by translating all the basic dynamic gates into the corresponding DBN model. The approach has been tested on a complex example taken from the literature. Our experimental results testify how DBN can be safely resorted to if a quantitative analysis of the system is required. Moreover, they are able to enhance both the modeling and the analysis capabilities of classical FT approaches, by representing more general depend-encies and by performing general inference on the resulting model.