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Importance sampling-based system reliability analysis of corroding pipelines considering multiple failure modes

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  • Gong, C.
  • Zhou, W.

Abstract

The importance sampling (IS) technique is employed to evaluate the time-dependent system reliability of corroding pipeline segments containing multiple active corrosion defects by considering two competing failure modes, small leak and burst. The IS density functions in the standard normal space for incremental probabilities of small leak and burst of the pipe segment over a short time period are established as the weighted averages of the IS density functions for small leak and burst, respectively, at individual corrosion defects. The IS density functions for incremental probabilities of small leak and burst of individual defects are centred at the design points in the corresponding failure domains. Four numerical examples that are representative of the onshore gas transmission pipelines in the US are used to illustrate the application of the proposed methodology. The results demonstrate the excellent accuracy and efficiency of the methodology.

Suggested Citation

  • Gong, C. & Zhou, W., 2018. "Importance sampling-based system reliability analysis of corroding pipelines considering multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 199-208.
    • Handle: RePEc:eee:reensy:v:169:y:2018:i:c:p:199-208
    DOI: 10.1016/j.ress.2017.08.023
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    References listed on IDEAS

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    1. Echard, B. & Gayton, N. & Lemaire, M. & Relun, N., 2013. "A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 232-240.
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    1. Shan, Xiangying & Yu, Weichao & Hu, Bing & Wen, Kai & Ren, Shipeng & Men, Yang & Li, Mingrui & Gong, Jing & Zheng, Honglong & Hong, Bingyuan, 2024. "A methodology to determine target gas supply reliability of natural gas pipeline system based on cost-benefit analysis," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    2. Liu, Aihua & Chen, Ke & Huang, Xiaofei & Li, Didi & Zhang, Xiaochun, 2021. "Dynamic risk assessment model of buried gas pipelines based on system dynamics," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    3. Kere, Kiswendsida J. & Huang, Qindan, 2024. "An analytical approach to evaluate life-cycle cost of deteriorating pipelines," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    4. Adumene, Sidum & Khan, Faisal & Adedigba, Sunday & Zendehboudi, Sohrab, 2021. "Offshore system safety and reliability considering microbial influenced multiple failure modes and their interdependencies," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. 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).
    6. Yu, Weichao & Huang, Weihe & Wen, Yunhao & Li, Yichen & Liu, Hongfei & Wen, Kai & Gong, Jing & Lu, Yanan, 2021. "An integrated gas supply reliability evaluation method of the large-scale and complex natural gas pipeline network based on demand-side analysis," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    7. An Li & Feng Jin & Yuan Li & Wen Lan & Pan Liu & Zhifeng Yu & Kai Wen, 2024. "A Reliability Assessment Method for Natural Gas Pipelines with Corroded Defects That Considers Detection Cycles," Energies, MDPI, vol. 17(14), pages 1-15, July.
    8. Li, Mingyang & Wang, Zequn, 2022. "LSTM-augmented deep networks for time-variant reliability assessment of dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    9. Keshtegar, Behrooz & Chakraborty, Souvik, 2018. "Dynamical accelerated performance measure approach for efficient reliability-based design optimization with highly nonlinear probabilistic constraints," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 69-83.
    10. Mohamed El Amine Ben Seghier & Panagiotis Spyridis & Jafar Jafari-Asl & Sima Ohadi & Xinhong Li, 2022. "Comparative Study on the Efficiency of Simulation and Meta-Model-Based Monte Carlo Techniques for Accurate Reliability Analysis of Corroded Pipelines," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
    11. Xiao, Rui & Zayed, Tarek & Meguid, Mohamed A. & Sushama, Laxmi, 2024. "Improving failure modeling for gas transmission pipelines: A survival analysis and machine learning integrated approach," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    12. Zhou, Yicheng & Lu, Zhenzhou & Yun, Wanying, 2020. "Active sparse polynomial chaos expansion for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    13. Gao, Haifeng & Wang, Anjenq & Zio, Enrico & Bai, Guangchen, 2020. "An integrated reliability approach with improved importance sampling for low-cycle fatigue damage prediction of turbine disks," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    14. Cao, Quoc Dung & Choe, Youngjun, 2019. "Cross-entropy based importance sampling for stochastic simulation models," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    15. 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).

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