Abstract
Fault Tree Analysis (FTA) is a widely adopted methodology where events are modeling the working/failure
dichotomy of components and subsystems. However, system variables are often of continuous nature, and in
some cases measured through a monitoring process. In this paper, we present an approach aimed at introducing
continuous variables in a standard static fault tree (FT) formalism. We show how continuous variables can be tied to
basic events in a FT, how to model probabilistic linear dependencies among them, and how influences of contextual
information on system variables can be captured and modeled. We called the resulting formalism c-FT, and we
propose a conversion of a c-FT into Hybrid Bayesian Networks (HBN); this allows us to exploit HBN inference
algorithms, in order to perform the analyses of interest on the modeled system. As an experimental framework, we
consider a model for a waste incinerator, and we present the results of specific analyses (from system reliability, to
posterior probability of faulty situations) implemented through conversion of a c-FT into an HBN and by exploiting
the MATLAB BNT Toolbox for inference.
Lingua originale | Inglese |
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Numero di pagine | 8 |
Stato di pubblicazione | Pubblicato - 1 gen 2020 |
Evento | 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference - Venice (Italy) Durata: 1 gen 2020 → … |
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???event.eventtypes.event.conference??? | 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference |
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Città | Venice (Italy) |
Periodo | 1/01/20 → … |
Keywords
- Fault Trees
- Bayesian Networks
- Hybrid Bayesian Networks
- Continuous Variables