IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v164y2017icp1-9.html
   My bibliography  Save this article

Accelerated Monte Carlo system reliability analysis through machine-learning-based surrogate models of network connectivity

Author

Listed:
  • Stern, R.E.
  • Song, J.
  • Work, D.B.

Abstract

The two-terminal reliability problem in system reliability analysis is known to be computationally intractable for large infrastructure graphs. Monte Carlo techniques can estimate the probability of a disconnection between two points in a network by selecting a representative sample of network component failure realizations and determining the source-terminal connectivity of each realization. To reduce the runtime required for the Monte Carlo approximation, this article proposes an approximate framework in which the connectivity check of each sample is estimated using a machine-learning-based classifier. The framework is implemented using both a support vector machine (SVM) and a logistic regression based surrogate model. Numerical experiments are performed on the California gas distribution network using the epicenter and magnitude of the 1989 Loma Prieta earthquake as well as randomly-generated earthquakes. It is shown that the SVM and logistic regression surrogate models are able to predict network connectivity with accuracies of 99% for both methods, and are 1–2 orders of magnitude faster than using a Monte Carlo method with an exact connectivity check.

Suggested Citation

  • Stern, R.E. & Song, J. & Work, D.B., 2017. "Accelerated Monte Carlo system reliability analysis through machine-learning-based surrogate models of network connectivity," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 1-9.
    • Handle: RePEc:eee:reensy:v:164:y:2017:i:c:p:1-9
    DOI: 10.1016/j.ress.2017.01.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832017301278
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2017.01.021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Der Kiureghian, Armen & Song, Junho, 2008. "Multi-scale reliability analysis and updating of complex systems by use of linear programming," Reliability Engineering and System Safety, Elsevier, vol. 93(2), pages 288-297.
    2. Stuart E. Dreyfus, 1969. "An Appraisal of Some Shortest-Path Algorithms," Operations Research, INFORMS, vol. 17(3), pages 395-412, June.
    3. Cook, Jason L. & Ramirez-Marquez, Jose Emmanuel, 2007. "Two-terminal reliability analyses for a mobile ad hoc wireless network," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 821-829.
    4. Peter W. Glynn & Donald L. Iglehart, 1989. "Importance Sampling for Stochastic Simulations," Management Science, INFORMS, vol. 35(11), pages 1367-1392, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Krupenev, Dmitry & Boyarkin, Denis & Iakubovskii, Dmitrii, 2020. "Improvement in the computational efficiency of a technique for assessing the reliability of electric power systems based on the Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    2. Lee, Dongkyu & Song, Junho, 2023. "Risk-informed operation and maintenance of complex lifeline systems using parallelized multi-agent deep Q-network," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    3. Xu, Jun & Wang, Ding, 2019. "Structural reliability analysis based on polynomial chaos, Voronoi cells and dimension reduction technique," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 329-340.
    4. Ying-Kui Gu & Chao-Jun Fan & Ling-Qiang Liang & Jun Zhang, 2022. "Reliability calculation method based on the Copula function for mechanical systems with dependent failure," Annals of Operations Research, Springer, vol. 311(1), pages 99-116, April.
    5. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    6. Bansal, Parth & Zheng, Zhuoyuan & Shao, Chenhui & Li, Jingjing & Banu, Mihaela & Carlson, Blair E & Li, Yumeng, 2022. "Physics-informed machine learning assisted uncertainty quantification for the corrosion of dissimilar material joints," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    7. Hernandez-Perdomo, Elvis & Guney, Yilmaz & Rocco, Claudio M., 2019. "A reliability model for assessing corporate governance using machine learning techniques," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 220-231.
    8. Li, Meng & Sadoughi, Mohammadkazem & Hu, Zhen & Hu, Chao, 2020. "A hybrid Gaussian process model for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    9. Jiang, Chen & Qiu, Haobo & Gao, Liang & Wang, Dapeng & Yang, Zan & Chen, Liming, 2020. "EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    10. Xu, Jun & Kong, Fan, 2018. "A new unequal-weighted sampling method for efficient reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 94-102.
    11. Liu, Xiaohang & Zheng, Shansuo & Wu, Xinxia & Chen, Dianxin & He, Jinchuan, 2021. "Research on a seismic connectivity reliability model of power systems based on the quasi-Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    12. Hu, Yingshi & Lu, Zhenzhou & Jiang, Xia & Wei, Ning & Zhou, Changcong, 2021. "Time-dependent structural system reliability analysis model and its efficiency solution," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    13. Simsekler, Mecit Can Emre & Rodrigues, Clarence & Qazi, Abroon & Ellahham, Samer & Ozonoff, Al, 2021. "A comparative study of patient and staff safety evaluation using tree-based machine learning algorithms," Reliability Engineering and System Safety, Elsevier, vol. 208(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Philippe Jehiel & Jakub Steiner, 2020. "Selective Sampling with Information-Storage Constraints [On interim rationality, belief formation and learning in decision problems with bounded memory]," The Economic Journal, Royal Economic Society, vol. 130(630), pages 1753-1781.
    2. Helton, J.C. & Hansen, C.W. & Sallaberry, C.J., 2014. "Conceptual structure and computational organization of the 2008 performance assessment for the proposed high-level radioactive waste repository at Yucca Mountain, Nevada," Reliability Engineering and System Safety, Elsevier, vol. 122(C), pages 223-248.
    3. Pierre L’Ecuyer & Bruno Tuffin, 2011. "Approximating zero-variance importance sampling in a reliability setting," Annals of Operations Research, Springer, vol. 189(1), pages 277-297, September.
    4. Prusty, B Rajanarayan & Jena, Debashisha, 2017. "A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1286-1302.
    5. Rocco S, Claudio M. & Ramirez-Marquez, José Emmanuel, 2009. "Deterministic network interdiction optimization via an evolutionary approach," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 568-576.
    6. Rodríguez, Joanna & Lillo, Rosa E. & Ramírez-Cobo, Pepa, 2015. "Failure modeling of an electrical N-component framework by the non-stationary Markovian arrival process," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 126-133.
    7. Dassios, Angelos & Jang, Jiwook & Zhao, Hongbiao, 2015. "A risk model with renewal shot-noise Cox process," Insurance: Mathematics and Economics, Elsevier, vol. 65(C), pages 55-65.
    8. Rolando Quintero & Esteban Mendiola & Giovanni Guzmán & Miguel Torres-Ruiz & Carlos Guzmán Sánchez-Mejorada, 2023. "Algorithm for the Accelerated Calculation of Conceptual Distances in Large Knowledge Graphs," Mathematics, MDPI, vol. 11(23), pages 1-30, November.
    9. Pijls, Wim & Post, Henk, 2009. "A new bidirectional search algorithm with shortened postprocessing," European Journal of Operational Research, Elsevier, vol. 198(2), pages 363-369, October.
    10. N. Hilber & N. Reich & C. Schwab & C. Winter, 2009. "Numerical methods for Lévy processes," Finance and Stochastics, Springer, vol. 13(4), pages 471-500, September.
    11. Steven K. Peterson & Richard L. Church, 2008. "A Framework for Modeling Rail Transport Vulnerability," Growth and Change, Wiley Blackwell, vol. 39(4), pages 617-641, December.
    12. Yang, Jinling & Chen, Zhiwei & Criado, Regino & Zhang, Shenggui, 2024. "A mathematical framework for shortest path length computation in multi-layer networks with inter-edge weighting and dynamic inter-edge weighting: The case of the Beijing bus network, China," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    13. Samet, Haidar & Khorshidsavar, Morteza, 2018. "Analytic time series load flow," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3886-3899.
    14. Delafield, Gemma & Smith, Greg S. & Day, Brett & Holland, Robert A. & Donnison, Caspar & Hastings, Astley & Taylor, Gail & Owen, Nathan & Lovett, Andrew, 2024. "Spatial context matters: Assessing how future renewable energy pathways will impact nature and society," Renewable Energy, Elsevier, vol. 220(C).
    15. Cook, Jason L. & Ramirez-Marquez, Jose Emmanuel, 2009. "Optimal design of cluster-based ad-hoc networks using probabilistic solution discovery," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 218-228.
    16. Dimitri P. Bertsekas, 2019. "Robust shortest path planning and semicontractive dynamic programming," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(1), pages 15-37, February.
    17. Sandeep Juneja & Perwez Shahabuddin, 2001. "Fast Simulation of Markov Chains with Small Transition Probabilities," Management Science, INFORMS, vol. 47(4), pages 547-562, April.
    18. Yueyue Fan & Yu Nie, 2006. "Optimal Routing for Maximizing the Travel Time Reliability," Networks and Spatial Economics, Springer, vol. 6(3), pages 333-344, September.
    19. Bensi, Michelle & Kiureghian, Armen Der & Straub, Daniel, 2013. "Efficient Bayesian network modeling of systems," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 200-213.
    20. Padmavathy, N. & Chaturvedi, Sanjay K., 2013. "Evaluation of mobile ad hoc network reliability using propagation-based link reliability model," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 1-9.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:164:y:2017:i:c:p:1-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.