Defect Identification Method for AUT Inspection of Pipeline Girth Welds Based on Depth Separable Residual Network

doi:10.3901/JME.260494

Abstract

Abstract: Automatic ultrasonic testing(AUT) technology is widely used for the inspection of pipeline girth welds. However, due to the influence of the proficiency level of the inspection personnel, it is prone to errors in judgment and missed detections. In order to reduce the influence of human factors on the accuracy and efficiency of defect identification, a defect identification method for pipeline girth weld based on depth separable convolution(DSC) improved ResNet is proposed. The scan signals of the pipeline girth weld obtained by AUT are preprocessed, and an image dataset is constructed as the input for the neural network. By weighting the loss function and replacing the second standard convolution in the residual modules of ResNet with DSC, a defect identification model called DS-ResNet is constructed based on DSC. The improved sparrow search algorithm(ISSA) is used to adaptively optimize hyperparameters such as learning rate, batch size, and maximum epochs in DS-ResNet. A comparative test is conducted between the proposed defect identification method and the network before optimization. The results indicate that the proposed identification method exhibits higher accuracy, precision, and F1-score. A feasible research approach is proposed for the intelligent identification of pipeline girth weld defects.

Key words: pipeline girth weld, defects identification, automatic ultrasonic testing, sparrow search algorithm, depth separable convolution, residual network

CLC Number:

TG441
TP181

LIANG Shitong, JIAO Jingpin, ZHANG Xiang. Defect Identification Method for AUT Inspection of Pipeline Girth Welds Based on Depth Separable Residual Network[J]. Journal of Mechanical Engineering, 2026, 62(10): 93-102.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260494

http://www.cjmenet.com.cn/EN/Y2026/V62/I10/93

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