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Sensitivity of system reliability of corroding pipelines to modeling of stochastic growth of corrosion defects

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  • Zhou, W.
  • Xiang, W.
  • Hong, H.P.

Abstract

The time-dependent system reliability of pressurized pipeline segments containing multiple active corrosion defects is evaluated to investigate the sensitivity of the system reliability to various modeling options regarding the corrosion growth. The gamma and inverse Gaussian processes are employed to model the defect growth, whereas the dependence among the growths of different defects is characterized using the Gaussian copula and sum-of-stochastic-process approach. The analysis results indicate that all else being equal the system reliability is insensitive to using the Gaussian copula or sum-of-stochastic-process approach to model the dependence among the growths of different defects. Furthermore, using the inverse Gaussian process to model the defect growth leads to slightly higher failure probabilities than using the gamma process. Finally, the results suggest that the use of one year as the time increment to simulate Gaussian copula-based dependent defect growths in the reliability analysis is adequate for the relatively slow corrosion growth that is typical for buried pipelines.

Suggested Citation

  • Zhou, W. & Xiang, W. & Hong, H.P., 2017. "Sensitivity of system reliability of corroding pipelines to modeling of stochastic growth of corrosion defects," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 428-438.
    • Handle: RePEc:eee:reensy:v:167:y:2017:i:c:p:428-438
    DOI: 10.1016/j.ress.2017.06.025
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    References listed on IDEAS

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    1. van Noortwijk, J.M., 2009. "A survey of the application of gamma processes in maintenance," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 2-21.
    2. Kallsen, Jan & Tankov, Peter, 2006. "Characterization of dependence of multidimensional Lévy processes using Lévy copulas," Journal of Multivariate Analysis, Elsevier, vol. 97(7), pages 1551-1572, August.
    3. Hong, H.P. & Zhou, W. & Zhang, S. & Ye, W., 2014. "Optimal condition-based maintenance decisions for systems with dependent stochastic degradation of components," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 276-288.
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    Cited by:

    1. Jiang, Fengyuan & Dong, Sheng, 2024. "Probabilistic-based burst failure mechanism analysis and risk assessment of pipelines with random non-uniform corrosion defects, considering the interacting effects," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    2. Miao, Xingyuan & Zhao, Hong, 2024. "Corroded submarine pipeline degradation prediction based on theory-guided IMOSOA-EL model," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. 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).
    4. Wu, Xin & Huang, Tingting & Liu, Jie, 2023. "Common stochastic effects induced multivariate degradation process with temporal dependency in degradation characteristic and unit dimensions," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    5. Xiangqin Hou & Yihuan Wang & Peng Zhang & Guojin Qin, 2019. "Non-Probabilistic Time-Varying Reliability-Based Analysis of Corroded Pipelines Considering the Interaction of Multiple Uncertainty Variables," Energies, MDPI, vol. 12(10), pages 1-18, May.
    6. Huynh, K.T. & Vu, H.C. & Nguyen, T.D. & Ho, A.C., 2022. "A predictive maintenance model for k-out-of-n:F continuously deteriorating systems subject to stochastic and economic dependencies," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. Wang, Sheng & Hui, Hongxun & Ding, Yi & Song, Yonghua, 2024. "Long-term reliability evaluation of integrated electricity and gas systems considering distributed hydrogen injections," Applied Energy, Elsevier, vol. 356(C).

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