TY - GEN
T1 - Comparing fault trees and bayesian networks for dependability analysis
AU - Bobbio, Andrea
AU - Portinale, Luigi
AU - Minichino, Michele
AU - Ciancamerla, Ester
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1999.
PY - 1999
Y1 - 1999
N2 - Bayesian Networks (BN) provide a robust probabilistic method of reasoning under uncertainty. They have been successfully applied in a variety of real-world tasks and their suitability for dependability analysis is now considered by several researchers. In the present paper, we aim at defining a formal comparison between BN and one of the most popular techniques for dependability analysis: Fault Trees (FT). We will show that any FT can be easily mapped into a BN and that basic inference techniques on the latter may be used to obtain classical parameters computed using the former (i.e. reliability of the Top Event or of any sub-system, criticality of components, etc...). Moreover, we will discuss how, by using BN, some additional power can be obtained, both at the modeling and at the analysis level. In particular, dependency among components and noisy gates can be easily accommodated in the BN framework, together with the possibility of performing general diagnostic analysis. The comparison of the two methodologies is carried on through the analysis of an example that consists of a redundant multiprocessor system, with local and shared memories, local mirrored disks and a single bus.
AB - Bayesian Networks (BN) provide a robust probabilistic method of reasoning under uncertainty. They have been successfully applied in a variety of real-world tasks and their suitability for dependability analysis is now considered by several researchers. In the present paper, we aim at defining a formal comparison between BN and one of the most popular techniques for dependability analysis: Fault Trees (FT). We will show that any FT can be easily mapped into a BN and that basic inference techniques on the latter may be used to obtain classical parameters computed using the former (i.e. reliability of the Top Event or of any sub-system, criticality of components, etc...). Moreover, we will discuss how, by using BN, some additional power can be obtained, both at the modeling and at the analysis level. In particular, dependency among components and noisy gates can be easily accommodated in the BN framework, together with the possibility of performing general diagnostic analysis. The comparison of the two methodologies is carried on through the analysis of an example that consists of a redundant multiprocessor system, with local and shared memories, local mirrored disks and a single bus.
UR - http://www.scopus.com/inward/record.url?scp=84961362208&partnerID=8YFLogxK
U2 - 10.1007/3-540-48249-0_27
DO - 10.1007/3-540-48249-0_27
M3 - Conference contribution
SN - 3540664882
SN - 9783540664888
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 310
EP - 322
BT - Computer Safety, Reliability and Security - 18th International Conference, SAFECOMP 1999, Proceedings
A2 - Felici, Massimo
A2 - Pasquini, Alberto
A2 - Kanoun, Karama
PB - Springer Verlag
T2 - 18th International Conference on Computer Safety, Reliability and Security, SAFECOMP 1999
Y2 - 27 September 1999 through 29 September 1999
ER -