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Formalism and semantics of PyCATSHOO: A simulator of distributed stochastic hybrid automata

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  • Desgeorges, Loïc
  • Piriou, Pierre-Yves
  • Lemattre, Thibault
  • Chraibi, Hassane

Abstract

This article lays the mathematical foundations of PyCATSHOO, a Model-Based Safety Analysis (MBSA) framework relying on distributed stochastic hybrid automata. This tool was initially developed for use cases where continuous evolution of physical variables or component failure rates matter to assess the dependability attributes. The modelling language has been designed in order to provide to the analyst the best expressiveness and ease of use. Nevertheless, although the structure and behaviour of a PyCATSHOO model have been informally described previously, they have never been formally established, which precludes its scientific acceptance and slows down its adoption by new users. To fill this lack, this article introduces formal definitions of the structure of PyCATSHOO models using set theory and of their operational semantics using inference rules (exactly 1 axiom and eight inference rules). These formal definitions are illustrated on a simple case study: the heated room. As a result, our proposing disambiguates the semantics of PyCATSHOO models, provides a formal specification of its input language and the core logic of its simulator engine and paves the way to the integration of model checking techniques in the PyCATSHOO framework.

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  • Desgeorges, Loïc & Piriou, Pierre-Yves & Lemattre, Thibault & Chraibi, Hassane, 2021. "Formalism and semantics of PyCATSHOO: A simulator of distributed stochastic hybrid automata," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:reensy:v:208:y:2021:i:c:s0951832020308711
    DOI: 10.1016/j.ress.2020.107384
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    References listed on IDEAS

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    1. Piriou, Pierre-Yves & Faure, Jean-Marc & Lesage, Jean-Jacques, 2017. "Generalized Boolean logic Driven Markov Processes: A powerful modeling framework for Model-Based Safety Analysis of dynamic repairable and reconfigurable systems," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 57-68.
    2. Boiteau, M. & Dutuit, Y. & Rauzy, A. & Signoret, J.-P., 2006. "The AltaRica data-flow language in use: modeling of production availability of a multi-state system," Reliability Engineering and System Safety, Elsevier, vol. 91(7), pages 747-755.
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    Cited by:

    1. Cheng, Ruijun & Cheng, Yu & Chen, Dewang & Song, Haifeng, 2021. "Online quantitative safety monitoring approach for unattended train operation system considering stochastic factors," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Hu, Yunpeng & Peng, Qibo & Ni, Qing & Wu, Xinfeng & Ye, Dongming, 2023. "Event-based safety and reliability analysis integration in model-based space mission design," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    3. Park, Jong Woo & Lee, Seung Jun, 2022. "Simulation optimization framework for dynamic probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    4. Vasilyev, A. & Andrews, J. & Dunnett, S.J. & Jackson, L.M., 2021. "Dynamic Reliability Assessment of PEM Fuel Cell Systems," Reliability Engineering and System Safety, Elsevier, vol. 210(C).

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