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Matching of corroded defects in onshore pipelines based on In-Line Inspections and Voronoi partitions

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  • Amaya-Gómez, Rafael
  • Schoefs, Franck
  • Sánchez-Silva, Mauricio
  • Muñoz, Felipe
  • Bastidas-Arteaga, Emilio

Abstract

Onshore pipelines are usually subjected to a corrosion attack. Regular inspections known as In-Line inspections (ILI) are commonly used with magnetic (MFL) or ultrasonic (UT) tools to prevent any failure. New defects will appear between consecutive inspections due to the aggressiveness of the surroundings and the detection thresholds associated with the defects’ depth. This work focuses on the matching problem between two inspections, aiming to identify the degradation increments and the position of new defects. Typically, it is linked to the well-known point matching problem in pattern recognition, where the objective is finding the best affine transformation between two sets of points in a plane. This work presents an alternative using Voronoi cells to filter possible matches and an iterative approach to determine the best affine transformation, considering the uncertainty in any direction. The approach was implemented for a real pipeline 45 km long and for synthetic corrosion defects, allowing us to identify possible matches easily. Based on the new and old defects, some insights about the probability of detection and false alarm are deduced. For this purpose, experimental probability and results from recognized exponential and log–logistic functions were considered.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:223:y:2022:i:c:s0951832022001752
    DOI: 10.1016/j.ress.2022.108520
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    References listed on IDEAS

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    Cited by:

    1. Miao, Xingyuan & Zhao, Hong, 2023. "Novel method for residual strength prediction of defective pipelines based on HTLBO-DELM model," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. 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).

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