Analysis and prediction of pipeline corrosion defects based on data analytics of in-line inspection

Analysis and prediction of pipeline corrosion defects based on data analytics of in-line inspection

Abstract In-line inspection (ILI) is important to pipeline integrity management since it can detect pipeline defects and identify potential failure locations through periodical examinations. However, effectively evaluating defects based on ILI data is challenging. Measurements of ILI are easily infl...

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Bibliographic Details
Main Authors: Bingyan Cui, Hao Wang
Format: Article
Language:English
Published: SpringerOpen 2023-06-01
Series:Journal of Infrastructure Preservation and Resilience
Subjects:
Steel pipeline
In-line inspection
Corrosion
Interacting effect
Machine learning
Stochastic growth model
Online Access:https://doi.org/10.1186/s43065-023-00081-w

Internet

https://doi.org/10.1186/s43065-023-00081-w

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