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Application of subset simulation in reliability estimation of underground pipelines

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  • Tee, Kong Fah
  • Khan, Lutfor Rahman
  • Li, Hongshuang

Abstract

This paper presents a computational framework for implementing an advanced Monte Carlo simulation method, called Subset Simulation (SS) for time-dependent reliability prediction of underground flexible pipelines. The SS can provide better resolution for low failure probability level of rare failure events which are commonly encountered in pipeline engineering applications. Random samples of statistical variables are generated efficiently and used for computing probabilistic reliability model. It gains its efficiency by expressing a small probability event as a product of a sequence of intermediate events with larger conditional probabilities. The efficiency of SS has been demonstrated by numerical studies and attention in this work is devoted to scrutinise the robustness of the SS application in pipe reliability assessment and compared with direct Monte Carlo simulation (MCS) method. Reliability of a buried flexible steel pipe with time-dependent failure modes, namely, corrosion induced deflection, buckling, wall thrust and bending stress has been assessed in this study. The analysis indicates that corrosion induced excessive deflection is the most critical failure event whereas buckling is the least susceptible during the whole service life of the pipe. The study also shows that SS is robust method to estimate the reliability of buried pipelines and it is more efficient than MCS, especially in small failure probability prediction.

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  • Tee, Kong Fah & Khan, Lutfor Rahman & Li, Hongshuang, 2014. "Application of subset simulation in reliability estimation of underground pipelines," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 125-131.
    • Handle: RePEc:eee:reensy:v:130:y:2014:i:c:p:125-131
    DOI: 10.1016/j.ress.2014.05.006
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    References listed on IDEAS

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    1. Kong Fah Tee & Lutfor Rahman Khan, 2014. "Reliability analysis of underground pipelines with correlations between failure modes and random variables," Journal of Risk and Reliability, , vol. 228(4), pages 362-370, August.
    2. Thomas Fetz & Fulvio Tonon, 2008. "Probability bounds for series systems with variables constrained by sets of probability measures," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 2(4), pages 309-339.
    3. Song, Shufang & Lu, Zhenzhou & Qiao, Hongwei, 2009. "Subset simulation for structural reliability sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 658-665.
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    Citations

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    Cited by:

    1. Yu, Weichao & Huang, Weihe & Wen, Kai & Zhang, Jie & Liu, Hongfei & Wang, Kun & Gong, Jing & Qu, Chunxu, 2021. "Subset simulation-based reliability analysis of the corroding natural gas pipeline," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    2. Kong Fah Tee & Andrew Utomi Ebenuwa, 2019. "Combination of line sampling and important sampling for reliability assessment of buried pipelines," Journal of Risk and Reliability, , vol. 233(2), pages 139-150, April.
    3. Yahui Zhang & Wei Wang & Huajiang Ouyang, 2019. "Dynamic reliability evaluation of vehicle–track coupled systems considering the randomness of suspension and wheel–rail parameters," Journal of Risk and Reliability, , vol. 233(6), pages 1106-1121, December.
    4. Zhou, Xingyuan & van Gelder, P.H.A.J.M. & Liang, Yongtu & Zhang, Haoran, 2020. "An integrated methodology for the supply reliability analysis of multi-product pipeline systems under pumps failure," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    5. 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.
    6. Kong Fah Tee & Lutfor Rahman Khan & Tahani Coolen-Maturi, 2015. "Application of receiver operating characteristic curve for pipeline reliability analysis," Journal of Risk and Reliability, , vol. 229(3), pages 181-192, June.
    7. Li, Yuyin & Zhang, Yahui & Kennedy, David, 2018. "Reliability analysis of subsea pipelines under spatially varying ground motions by using subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 74-83.
    8. Liu, Fuchao & Wei, Pengfei & Tang, Chenghu & Wang, Pan & Yue, Zhufeng, 2019. "Global sensitivity analysis for multivariate outputs based on multiple response Gaussian process model," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 287-298.

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