Italiano (Italian) English (Inglese)
venerdì, 15 dicembre 2017

Pubblicazioni

Indietro
Dettagli Pubblicazione
Autori:Daniele Codetta Raiteri
Stefania Montani
Luigi Portinale
Area Scientifica:Artificial Intelligence
Uncertain Reasoning
Probabilistic Graphical Models
Dependability and Reliability
Titolo:Supporting Reliability Engineers in Exploiting the Power of Dynamic Bayesian Networks
Apparso su:International Journal of Approximate Reasoning vol. 51(2)
Pagine:179-195
Editore:Elsevier
Anno:2010
Tipo Pubblicazione:Paper on International Journal
URL:http://dx.doi.org/10.1016/j.ijar.2009.05.009
Sommario:In this paper, we present an approach to reliability modeling and analysis based on the automatic conversion of a particular reliability engineering model, the Dynamic Fault Tree (DFT), into Dynamic Bayesian Networks (DBN). The approach is implemented in a software tool called RADYBAN (Reliability Analysis with DYnamic BAyesian Networks). The aim is to provide a familiar interface to reliability engineers, by allowing them to model the system to be analyzed with a standard formalism; however, a modular algorithm is implemented to automatically compile a DFT into the corresponding DBN. In fact, when the computation of specific reliability measures is requested, classical algorithms for the inference on Dynamic Bayesian Networks are exploited, in order to compute the requested parameters. This is performed in a totally transparent way to the user, who could in principle be completely unaware of the underlying Bayesian Network. The use of DBNs allows the user to be able to compute measures that are not directly computable from DFTs, but that are naturally obtainable from DBN inference. Moreover, the modeling capabilities of a DBN, allow us to extend the basic DFT formalism, by introducing probabilistic dependencies among system components, as well as the definition of specific repair policies that can be taken into account during the reliability analysis phase. We finally show how the approach operates on some specific examples, by describing the advantages of having available a full inference engine based on DBNs for the requested analysis tasks.