|Scientific Area:||Uncertain Reasoning|
|Probabilistic Graphical Models|
|Dependability and Reliability|
|Title:||Dynamic Bayesian Networks for Modeling Advanced Fault Tree Features in Dependability Analisys|
|Publisher:||DiSIT, Computer Science Institute, UPO|
|Tipo Pubblicazione:||Technical Report|
|Abstract:||Fault Trees (FT) are one of the most popular techniques for dependability analysis of large, safety critical systems. It has been shown that FT can be directly mapped into Bayesian Networks (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 local dependencies and by performing general inference on the resulting model.|