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Deep learning-based identification of pipeline weld defects using automated ultrasonic testing.

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Title: Deep learning-based identification of pipeline weld defects using automated ultrasonic testing.
Authors: Wu, Gang1 (AUTHOR) wugang1199@126.com, Luo, Jinheng1 (AUTHOR), Wang, Manqi2 (AUTHOR), Xie, Shuyi1 (AUTHOR), Liang, Shitong3 (AUTHOR), Jiao, Jingpin3 (AUTHOR), Wang, Bohong2,4 (AUTHOR) wangbh@zjou.edu.cn
Source: Nondestructive Testing & Evaluation. Apr2026, Vol. 41 Issue 4, p2321-2342. 22p.
Subjects: Ultrasonic testing, Welding defects, Principal components analysis, Deep learning, Artificial neural networks, Feature extraction, Optimization algorithms
Abstract: For the safety evaluation of pipe welds, this paper proposes an automated ultrasonic testing method focused on defect signal feature extraction and identification. First, defect features are extracted based on the ultrasonic scattering coefficient distribution. The defect scattering coefficient matrix is then compressed using principal component analysis (PCA) to obtain the feature vector that best represents the defect. A Depthwise Separable Residual Network (DS-ResNet) model is constructed to identify pipe weld defects automatically. The sparrow search algorithm (SSA) is integrated with DS-ResNet (SSA-DS-ResNet) to optimise the model and enhance its performance. This method is applied to a case study, yielding a prediction accuracy of 97.51%, which is acceptable for industrial applications. The performance of SSA-DS-ResNet was compared with two networks prior to optimisation (ResNet and DS-ResNet), and the results indicate that SSA-DS-ResNet achieves higher accuracy. [ABSTRACT FROM AUTHOR]
Copyright of Nondestructive Testing & Evaluation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Deep learning-based identification of pipeline weld defects using automated ultrasonic testing.
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  Data: <searchLink fieldCode="AR" term="%22Wu%2C+Gang%22">Wu, Gang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wugang1199@126.com</i><br /><searchLink fieldCode="AR" term="%22Luo%2C+Jinheng%22">Luo, Jinheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Manqi%22">Wang, Manqi</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xie%2C+Shuyi%22">Xie, Shuyi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liang%2C+Shitong%22">Liang, Shitong</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiao%2C+Jingpin%22">Jiao, Jingpin</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Bohong%22">Wang, Bohong</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<i> wangbh@zjou.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Nondestructive+Testing+%26+Evaluation%22">Nondestructive Testing & Evaluation</searchLink>. Apr2026, Vol. 41 Issue 4, p2321-2342. 22p.
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  Data: <searchLink fieldCode="DE" term="%22Ultrasonic+testing%22">Ultrasonic testing</searchLink><br /><searchLink fieldCode="DE" term="%22Welding+defects%22">Welding defects</searchLink><br /><searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: For the safety evaluation of pipe welds, this paper proposes an automated ultrasonic testing method focused on defect signal feature extraction and identification. First, defect features are extracted based on the ultrasonic scattering coefficient distribution. The defect scattering coefficient matrix is then compressed using principal component analysis (PCA) to obtain the feature vector that best represents the defect. A Depthwise Separable Residual Network (DS-ResNet) model is constructed to identify pipe weld defects automatically. The sparrow search algorithm (SSA) is integrated with DS-ResNet (SSA-DS-ResNet) to optimise the model and enhance its performance. This method is applied to a case study, yielding a prediction accuracy of 97.51%, which is acceptable for industrial applications. The performance of SSA-DS-ResNet was compared with two networks prior to optimisation (ResNet and DS-ResNet), and the results indicate that SSA-DS-ResNet achieves higher accuracy. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Nondestructive Testing & Evaluation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1080/10589759.2025.2505094
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      – Code: eng
        Text: English
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    Subjects:
      – SubjectFull: Ultrasonic testing
        Type: general
      – SubjectFull: Welding defects
        Type: general
      – SubjectFull: Principal components analysis
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Artificial neural networks
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      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Optimization algorithms
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    Titles:
      – TitleFull: Deep learning-based identification of pipeline weld defects using automated ultrasonic testing.
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              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
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