A compositional semantics for repairable fault trees with general distributions

Raúl Monti*, Carlos E. Budde*, Pedro R. D’argenio

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

Fault Tree Analysis (FTA) is a prominent technique in industrial and scientific risk assessment. Repairable Fault Trees (RFT) enhance the classical Fault Tree (FT) model by introducing the possibility to describe complex dependent repairs of system components. Usual frameworks for analyzing FTs such as BDD, SBDD, and Markov chains fail to assess the desired properties over RFT complex models, either because these become too large, or due to cyclic behaviour introduced by dependent repairs. Simulation is another way to carry out this kind of analysis. In this paper we review the RFT model with Repair Boxes as introduced by Daniele Codetta-Raiteri. We present compositional semantics for this model in terms of Input/Output Stochastic Automata, which allows for the modelling of events occurring according to general continuous distribution. Moreover, we prove that the semantics generates (weakly) deterministic models, hence suitable for discrete event simulation, and prominently for rare event simulation using the FIG tool.

Original languageEnglish
Title of host publicationProceedings of the 23rd International Conference on Logic for Programming, Artificial Intelligence and Reasoning
EditorsElvira Albert, Laura Kovacs
PublisherEasyChair
Pages354-372
Number of pages19
DOIs
Publication statusPublished - 27 May 2020
Event23rd International Conference on Logic for Programming, Artificial Intelligence and Reasoning, LPAR23 2020 - Virtual Conference, Alicante, Spain
Duration: 22 May 202027 May 2020
Conference number: 23

Publication series

NameEPiC Series in Computing
Volume73

Conference

Conference23rd International Conference on Logic for Programming, Artificial Intelligence and Reasoning, LPAR23 2020
Abbreviated titleLPAR23 2020
CountrySpain
CityAlicante
Period22/05/2027/05/20

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