Neural network data analysis for ultrasonic non-destructive testing of main gas pipelines

DOI: 10.21293/1818-0442-2024-27-4-136-140

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Abstract: The corrosion process stems pipeline wall thinning to the critical values. Considering this the article aimed to look into a problem of detection a corrosion zone for a main gas pipeline metal wall using an ultrasonic non-destructive testing data. The proposed solution is to use one of the machine learning algorithms – convolutional neural network – to handle data obtained by an in-tube ultrasonic analyzer.

Keywords: neural network analysis, convolutional neural networks, ultrasonic non-destructive testing

For citation:
Borovskoy I. G., Matolygin A. A., Ilin E. P. Neural network data analysis for ultrasonic non-destructive testing of main gas pipelines. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, 2024, vol. 27, no. 4, pp. 136–140. DOI: 10.21293/1818-0442-2024-27-4-136-140

Authors and copyright holders:

  • Borovskoy I. G. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)
  • Matolygin A. A. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)
  • Ilin E. P. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)

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