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Numerically efficient computation of the survival signature for the reliability analysis of large networks

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  • Behrensdorf, Jasper
  • Regenhardt, Tobias-Emanuel
  • Broggi, Matteo
  • Beer, Michael

Abstract

Societal growth thrives on the performance of critical infrastructure systems such as water supply systems, transportation networks or electrical distribution systems. This makes the reliability analysis of these systems a core focus for researchers today. The survival signature is a novel tool for analysing complex networks efficiently and outperforms traditional techniques in several key factors. Its most unique feature being a full separation of the system structure from probabilistic information. This in turn allows for the consideration of diverse component failure descriptions such as dependencies, common causes of failure and imprecise probabilities. However, the numerical effort to compute the survival signature increases with network size and prevents analysis of complex systems. This work presents a new method to approximate the survival signature, where system configurations of low interest are first excluded using percolation theory, while the remaining parts of the signature are approximated by Monte Carlo simulation. The approach is able to accurately approximate the survival signature with very small error at a massive reduction in computational demands. The accuracy and performance are highlighted using several simple test systems as well as two real world problems.

Suggested Citation

  • Behrensdorf, Jasper & Regenhardt, Tobias-Emanuel & Broggi, Matteo & Beer, Michael, 2021. "Numerically efficient computation of the survival signature for the reliability analysis of large networks," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
  • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021004464
    DOI: 10.1016/j.ress.2021.107935
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    References listed on IDEAS

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    10. Patelli, Edoardo & Feng, Geng & Coolen, Frank P.A. & Coolen-Maturi, Tahani, 2017. "Simulation methods for system reliability using the survival signature," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 327-337.
    11. Huang, Xianzhen & Aslett, Louis J.M. & Coolen, Frank P.A., 2019. "Reliability analysis of general phased mission systems with a new survival signature," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 416-422.
    12. Hindolo George-Williams & Geng Feng & Frank PA Coolen & Michael Beer & Edoardo Patelli, 2019. "Extending the survival signature paradigm to complex systems with non-repairable dependent failures," Journal of Risk and Reliability, , vol. 233(4), pages 505-519, August.
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    14. Nan, Cen & Sansavini, Giovanni, 2017. "A quantitative method for assessing resilience of interdependent infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 35-53.
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    Cited by:

    1. Zhang, Jiarui & Tang, Bin & Duan, Yuxian & Huang, Jian, 2023. "Percolation phase transition in the heterogeneous multi-coupled interdependent network," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    2. Du, Yi-Mu & Sun, C.P., 2022. "A novel interpretable model of bathtub hazard rate based on system hierarchy," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    3. Chang, Miaoxin & Huang, Xianzhen & Coolen, Frank PA & Coolen-Maturi, Tahani, 2023. "New reliability model for complex systems based on stochastic processes and survival signature," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1349-1364.
    4. Wang, Shaoxuan & Yao, Yuantao & Ge, Daochuan & Lin, Zhixian & Wu, Jie & Yu, Jie, 2023. "Reliability evaluation of standby redundant systems based on the survival signatures methods," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    5. Shi, Yan & Behrensdorf, Jasper & Zhou, Jiayan & Hu, Yue & Broggi, Matteo & Beer, Michael, 2024. "Network reliability analysis through survival signature and machine learning techniques," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    6. Rusnak, Patrik & Zaitseva, Elena & Levashenko, Vitaly & Bolvashenkov, Igor & Kammermann, Jörg, 2024. "Importance analysis of a system based on survival signature by structural importance measures," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    7. Monfared, M.A.S. & Rezazadeh, Masoumeh & Alipour, Zohreh, 2022. "Road networks reliability estimations and optimizations: A Bi-directional bottom-up, top-down approach," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    8. Salomon, Julian & Winnewisser, Niklas & Wei, Pengfei & Broggi, Matteo & Beer, Michael, 2021. "Efficient reliability analysis of complex systems in consideration of imprecision," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    9. Maryam Kelkinnama & Serkan Eryilmaz, 2023. "Some reliability measures and maintenance policies for a coherent system composed of different types of components," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(1), pages 57-82, January.
    10. Di Maio, Francesco & Pettorossi, Chiara & Zio, Enrico, 2023. "Entropy-driven Monte Carlo simulation method for approximating the survival signature of complex infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 231(C).

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