Reconstruction of 3-D pipeline corrosion defect profile from MFL signals with deep learning approach-Reference-Cited by-同舟云学术

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Reconstruction of 3-D pipeline corrosion defect profile from MFL signals with deep learning approach

Published:2024-05-09 Issue: Volume:84447 Page:12
ISSN:
Container-title:NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE II
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Author:

Ling Jiatong,Peng Xiang,Siggers Kevin,Peussner Matthias,Rayhana Rakiba,Liu Zheng

Publisher

SPIE

Reference27 articles.

1. Disrupting the flow? a step change in burst pressure accuracy optimizes repair schedules;Wilde,2021

2. IEEE Transactions on Magnetics

3. IEEE Transactions on Magnetics

4. Defect profile reconstruction from MFL signals based on a specially-designed genetic taboo search algorithm

5. Enhancing results of ultra-high-resolution axial magnetic flux leakage (mfl-a ultra) inspection data utilizing finite-element modeling (fem) simulations;Schneider,2022

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