Development of a convolutional neural network model to predict the size and location of corrosion defects on pipelines based on magnetic flux leakage signals-Reference-Cited by-同舟云学术

Development of a convolutional neural network model to predict the size and location of corrosion defects on pipelines based on magnetic flux leakage signals

Published:2024-02 Issue: Volume:207 Page:105123
ISSN:0308-0161
Container-title:International Journal of Pressure Vessels and Piping
language:en
Short-container-title:International Journal of Pressure Vessels and Piping

Author:

Shen Y.,Zhou W.^ORCID

Funder

Western University

Natural Sciences and Engineering Research Council of Canada

Publisher

Elsevier BV

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

Reference65 articles.

1. A review on pipeline corrosion, in-line inspection (ILI), and corrosion growth rate models，Int;Vanaei;J. Press. Vessels Pip.,2017

2. Comparative analysis of in-line inspection equipments and technologies, in: IOP Conference series: materials science and engineering;Song;IOP Publishing,2018

3. Analysis of magnetic-flux leakage (MFL) data for pipeline corrosion assessment;Peng;IEEE Trans. Magn.,2020

4. Fast magnetic flux leakage signal inversion for the reconstruction of arbitrary defect profiles in steel using finite elements;Priewald;IEEE Trans. Magn.,2012

5. Magnetic flux leakage signal inversion based on improved efficient population utilization strategy for particle swarm optimization;Han;Russ. J. Nondestr. Test.,2017

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