Development of a convolutional neural network model to predict the size and location of corrosion defects on pipelines based on magnetic flux leakage signals
Author:
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
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3