Reliability analysis of corroded pipes using MFL signals and Residual Neural Networks

Author:

Chen YinuoORCID,Tian Zhigang,Wei Haotian,Dong Shaohua

Funder

China Scholarship Council

Publisher

Elsevier BV

Reference55 articles.

1. A risk-based approach to determination of optimal inspection intervals for buried oil pipelines;Abubakirov;Process Saf. Environ. Prot.,2020

2. Predictive deep learning for pitting corrosion modeling in buried transmission pipelines;Akhlaghi;Process Saf. Environ. Prot.,2023

3. Integrity assessment of corroded pipelines using dynamic segmentation and clustering;Amaya-Gómez;Process Saf. Environ. Prot.,2019

4. Benjamin, A., Andrade, E., 2003. Benjamin, A., Andrade, E., 2003. Modified Method for the Assessment of the Remaining Strength of Corroded Pipelines.

5. 10 - In-line inspection (ILI) methods for detecting corrosion in underground pipelines;Brockhaus,2014

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