A compositional semantics for Repairable Fault Trees with general distributions

Raúl E. Monti; Carlos E. Budde; Pedro R. D'Argenio

doi:10.29007/p16v

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A compositional semantics for Repairable Fault Trees with general distributions

19 pages•Published: May 27, 2020

Raúl E. Monti, Carlos E. Budde and Pedro R. D'Argenio

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.

Keyphrases: Analysis by simulation, Dynamic Fault Trees, Fault Tree Analysis, Input/Output Stochastic Automata, Repairable Fault Trees, semantic model, Weak determinism

In: Elvira Albert and Laura Kovács (editors). LPAR23. LPAR-23: 23rd International Conference on Logic for Programming, Artificial Intelligence and Reasoning, vol 73, pages 354--372

Links:	https://easychair.org/publications/paper/SWBh
	https://doi.org/10.29007/p16v

BibTeX entry

@inproceedings{LPAR23:compositional_semantics_for_Repairable,
  author    = {Ra\textbackslash{}'ul E. Monti and Carlos E. Budde and Pedro R. D'Argenio},
  title     = {A compositional semantics for Repairable Fault Trees with general distributions},
  booktitle = {LPAR23. LPAR-23: 23rd International Conference on Logic for Programming, Artificial Intelligence and Reasoning},
  editor    = {Elvira Albert and Laura Kovacs},
  series    = {EPiC Series in Computing},
  volume    = {73},
  pages     = {354--372},
  year      = {2020},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/SWBh},
  doi       = {10.29007/p16v}}

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