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Stochastic corrosion growth modeling for pipelines using mass inspection data

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  • Dann, Markus R.
  • Maes, Marc A.

Abstract

Integrity assessment of corroded pipelines requires estimates of the current and future sizes of the features. Corrosion growth is often inferred from inspection results by analyzing the feature-specific growth path. The objective is to introduce a new probabilistic model to determine the current and future metal loss for corroded pipelines based on mass inspection data. The model treats the corrosion features from a population perspective without tracking the local growth of each feature. Measurement errors such as detectability, false calls, and sizing errors are considered to infer the population of actual features from the inspection data. Two separate stochastic gamma processes are applied to model corrosion growth of the already existing and new features between inspections. The proposed population-based model does not require feature matching compared to a feature-specific corrosion growth analysis. The developed model is ideal for pipelines with high feature densities where feature matching can be time intensive and prone to errors. The problem size in the proposed model is independent of the number of observed features and, consequently, efficient data processing is guaranteed. The obtained analysis results are often sufficient to manage the integrity of pipelines without the increased effort of a feature-specific corrosion growth analysis.

Suggested Citation

  • Dann, Markus R. & Maes, Marc A., 2018. "Stochastic corrosion growth modeling for pipelines using mass inspection data," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 245-254.
    • Handle: RePEc:eee:reensy:v:180:y:2018:i:c:p:245-254
    DOI: 10.1016/j.ress.2018.07.012
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    References listed on IDEAS

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    1. Qin, H. & Zhou, W. & Zhang, S., 2015. "Bayesian inferences of generation and growth of corrosion defects on energy pipelines based on imperfect inspection data," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 334-342.
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    4. Gomes, Wellison J.S. & Beck, André T. & Haukaas, Terje, 2013. "Optimal inspection planning for onshore pipelines subject to external corrosion," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 18-27.
    5. 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.
    6. Zhang, Shenwei & Zhou, Wenxing, 2014. "Bayesian dynamic linear model for growth of corrosion defects on energy pipelines," Reliability Engineering and System Safety, Elsevier, vol. 128(C), pages 24-31.
    7. Dann, Markus R. & Dann, Christoph, 2017. "Automated matching of pipeline corrosion features from in-line inspection data," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 40-50.
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    Cited by:

    1. Chengtao Wang & Wei Li & Gaifang Xin & Yuqiao Wang & Shaoyi Xu, 2019. "Prediction Model of Corrosion Current Density Induced by Stray Current Based on QPSO-Driven Neural Network," Complexity, Hindawi, vol. 2019, pages 1-15, October.
    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. Zhang, Tieyao & Shuai, Jian & Shuai, Yi & Hua, Luoyi & Xu, Kui & Xie, Dong & Mei, Yuan, 2023. "Efficient prediction method of triple failure pressure for corroded pipelines under complex loads based on a backpropagation neural network," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    4. Heidary, Roohollah & Groth, Katrina M., 2021. "A hybrid population-based degradation model for pipeline pitting corrosion," Reliability Engineering and System Safety, Elsevier, vol. 214(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).

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