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Conception of Repairable Dynamic Fault Trees and resolution by the use of RAATSS, a Matlab® toolbox based on the ATS formalism

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  • Manno, G.
  • Chiacchio, F.
  • Compagno, L.
  • D'Urso, D.
  • Trapani, N.

Abstract

Dynamic Fault Tree (DFT) is a well-known stochastic technique for conducting reliability studies of complex systems. At the state of the art, existing tools (both academic and commercial) do not fully support DFT with repairable components and repeated events, lowering the penetration of this powerful technique in real industrial applications (e.g., industrial processes and plants, computer, electronic and network applications). One of the main reasons limiting the attractiveness of DFT is that, originally, DFTs were conceived without repairable components; only recently few related works have started to deal with a formal semantic, which would avoid undefined behavior and misinterpretation of DFT. Other researchers have tackled the problem by introducing extensions of the original Fault Trees (FTs) technique like Boolean Driven Markov Processes (BDMPs) and Generalized Fault Trees (GFTs). However, despite they consider repairable systems and repeated events, we have found that the introduction of a different formalism with more complex features has again limited the penetration of these powerful methods in real applications. The target of this work is the original DFT technique. Starting from the state of the art, a set of standardized rules that frame the behaviors of dynamic gates are designed and a well-defined semantic for repairable-DFT is drawn through the application of a novel formalism, the Adaptive Transitions System (ATS). The proposed theoretical framework is afterward used to code a software tool, RAATSS, for the resolution of extended, repairable-DFT. Moreover, this work introduces some novel concepts regarding the modeling of a system by a DFT and provides a basic hint of the ATS capabilities to describe interdependencies in complex system.

Suggested Citation

  • Manno, G. & Chiacchio, F. & Compagno, L. & D'Urso, D. & Trapani, N., 2014. "Conception of Repairable Dynamic Fault Trees and resolution by the use of RAATSS, a Matlab® toolbox based on the ATS formalism," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 250-262.
  • Handle: RePEc:eee:reensy:v:121:y:2014:i:c:p:250-262
    DOI: 10.1016/j.ress.2013.09.002
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    References listed on IDEAS

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    1. Codetta-Raiteri, Daniele, 2011. "Integrating several formalisms in order to increase Fault Trees' modeling power," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 534-544.
    2. Chiacchio, F. & Compagno, L. & D'Urso, D. & Manno, G. & Trapani, N., 2011. "Dynamic fault trees resolution: A conscious trade-off between analytical and simulative approaches," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1515-1526.
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    Citations

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    Cited by:

    1. Aslett, Louis J.M. & Nagapetyan, Tigran & Vollmer, Sebastian J., 2017. "Multilevel Monte Carlo for Reliability Theory," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 188-196.
    2. Chiacchio, Ferdinando & D’Urso, Diego & Famoso, Fabio & Brusca, Sebastian & Aizpurua, Jose Ignacio & Catterson, Victoria M., 2018. "On the use of dynamic reliability for an accurate modelling of renewable power plants," Energy, Elsevier, vol. 151(C), pages 605-621.
    3. Mi, Jinhua & Li, Yan-Feng & Yang, Yuan-Jian & Peng, Weiwen & Huang, Hong-Zhong, 2016. "Reliability assessment of complex electromechanical systems under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 1-15.
    4. Gascard, Eric & Simeu-Abazi, Zineb, 2018. "Quantitative Analysis of Dynamic Fault Trees by means of Monte Carlo Simulations: Event-Driven Simulation Approach," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 487-504.
    5. Ferdinando Chiacchio & Fabio Famoso & Diego D’Urso & Sebastian Brusca & Jose Ignacio Aizpurua & Luca Cedola, 2018. "Dynamic Performance Evaluation of Photovoltaic Power Plant by Stochastic Hybrid Fault Tree Automaton Model," Energies, MDPI, vol. 11(2), pages 1-22, January.
    6. Aizpurua, J.I. & Catterson, V.M. & Papadopoulos, Y. & Chiacchio, F. & D'Urso, D., 2017. "Supporting group maintenance through prognostics-enhanced dynamic dependability prediction," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 171-188.
    7. Chiacchio, F. & D’Urso, D. & Manno, G. & Compagno, L., 2016. "Stochastic hybrid automaton model of a multi-state system with aging: Reliability assessment and design consequences," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 1-13.
    8. Chemweno, Peter & Pintelon, Liliane & Muchiri, Peter Nganga & Van Horenbeek, Adriaan, 2018. "Risk assessment methodologies in maintenance decision making: A review of dependability modelling approaches," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 64-77.
    9. Chiacchio, Ferdinando & Iacono, Alessandra & Compagno, Lucio & D'Urso, Diego, 2020. "A general framework for dependability modelling coupling discrete-event and time-driven simulation," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    10. Xu, Jintao & Gui, Maolei & Ding, Rui & Dai, Tao & Zheng, Mengyan & Men, Xinhong & Meng, Fanpeng & Yu, Tao & Sui, Yang, 2023. "A new approach for dynamic reliability analysis of reactor protection system for HPR1000," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    11. Dingqing Guo & Jinkai Wang & Jian Lin & Bing Zhang & Nou Yong & Dongqin Xia & Daochuan Ge, 2023. "An adapted component-connection method for building SBDD encoding a dynamic fault tree," Journal of Risk and Reliability, , vol. 237(6), pages 1163-1174, December.
    12. Liang, Zhenglin & Liu, Bin & Xie, Min & Parlikad, Ajith Kumar, 2020. "Condition-based maintenance for long-life assets with exposure to operational and environmental risks," International Journal of Production Economics, Elsevier, vol. 221(C).

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