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Automated matching of pipeline corrosion features from in-line inspection data

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  • Dann, Markus R.
  • Dann, Christoph

Abstract

The integrity assessment of corroded pipelines is often based on in-line inspection (ILI) results. Before determining the corrosion growth for the integrity assessment, the detected corrosion features from two or more ILIs need to be matched with respect to their location in the pipeline. The objective of this paper is to introduce a framework for automated feature matching. The input for the framework is the locations of all detected corrosion features and girth welds from each ILI. Using a multi-step approach, the size of several ILIs with a possibly large number of features is reduced to a set of independent smaller problems to match efficiently the corrosion features. The results include the matched features for the subsequent corrosion growth analysis and the identification of outliers that cannot be matched. The applied probabilistic matching assigns to each feature pair a probability of being a match to reflect the inherent uncertainty in the matching process. The proposed framework replaces manual matching, which can be time intensive and prone to errors, particularly for internal corrosion with high feature densities. It reliably matches features in pipelines and supports the integrity and risk assessment of pipeline systems.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:162:y:2017:i:c:p:40-50
    DOI: 10.1016/j.ress.2017.01.008
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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.
    2. 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.
    3. 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.
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    Cited by:

    1. Amaya-Gómez, Rafael & Schoefs, Franck & Sánchez-Silva, Mauricio & Muñoz, Felipe & Bastidas-Arteaga, Emilio, 2022. "Matching of corroded defects in onshore pipelines based on In-Line Inspections and Voronoi partitions," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    2. 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.
    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. Mohd Fadly Hisham Ismail & Zazilah May & Vijanth Sagayan Asirvadam & Nazrul Anuar Nayan, 2023. "Machine-Learning-Based Classification for Pipeline Corrosion with Monte Carlo Probabilistic Analysis," Energies, MDPI, vol. 16(8), pages 1-13, April.
    5. 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).

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