Image Classification Method Based on Improved Deep Convolutional Neural Networks for the Magnetic Flux Leakage (MFL) Signal of Girth Welds in Long-Distance Pipelines

Liyuan Geng (), Shaohua Dong, Weichao Qian and Donghua Peng
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Liyuan Geng: College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102200, China
Shaohua Dong: College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102200, China
Weichao Qian: College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102200, China
Donghua Peng: PipeChina Company, Beijing 100020, China

Sustainability, 2022, vol. 14, issue 19, 1-21

Abstract: 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.

Keywords: 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) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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