论文成果
Image Classification Method Based on Improved Deep Convolutional Neural Networks for the Magnetic Flux Leakage (MFL) Signal of Girth Welds in Long-Distance Pipelines
摘要:Girth weld defects in long-distance oil and gas pipelines are one of the main causes of pipeline leakage failure and serious accidents. Magnetic flux leakage (MFL) is one of the most widely used inline inspection methods for long-distance pipelines. However, it is impossible to determine the type of girth weld defect via traditional manual analysis due to the complexity of the MFL signal. Therefore, an automatic image classification method based on deep convolutional neural networks was proposed to effectively classify girth weld defects via MFL signals. Firstly, the image data set of girth welds MFL signal was established with the radiographic testing results as labels. Then, the deep convolutional generative adversarial network (DCGAN) data enhancement algorithm was proposed to enhance the data set, and the residual network (ResNet-50) was proposed to address the challenge presented by the automatic classification of the image sets. The data set after data enhancement was randomly selected to train and test the improved residual network (ResNet-50), with the ten validation results exhibiting an accuracy of over 80%. The results indicated that the improved network model displayed a strong generalization ability and robustness and could achieve a more accurate MFL image classification of the pipeline girth welds.
关键字:pipeline girth weld; magnetic flux leakage (MFL) inline inspection; convolutional neural network (CNN); data enhancement; image classification; deep convolutional generative adversarial network (DCGAN); residual network (ResNet)
ISSN号:2071-1050
卷、期、页:卷14期19
发表日期:2022-10-01
影响因子:3.250600
期刊分区(SCI为中科院分区):三区
收录情况:SCI(科学引文索引印刷版),SSCI(社会科学引文索引),SCIE(科学引文索引网络版)
发表期刊名称:SUSTAINABILITY
参与作者:彭东华
通讯作者:耿丽媛,钱伟超
第一作者:董绍华
论文类型:期刊论文
论文概要:耿丽媛,董绍华,钱伟超,彭东华,Image Classification Method Based on Improved Deep Convolutional Neural Networks for the Magnetic Flux Leakage (MFL) Signal of Girth Welds in Long-Distance Pipelines,SUSTAINABILITY,2022,卷14期19
论文题目:Image Classification Method Based on Improved Deep Convolutional Neural Networks for the Magnetic Flux Leakage (MFL) Signal of Girth Welds in Long-Distance Pipelines