Neural network data analysis for ultrasonic non-destructive testing of main gas pipelines
DOI: 10.21293/1818-0442-2024-27-4-136-140
DOI: 10.21293/1818-0442-2024-27-4-136-140
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:
Executive Secretary of the Editor’s Office
Editor’s Office: 40 Lenina Prospect, Tomsk, 634050, Russia
Phone / Fax: + 7 (3822) 701-582
Executive Secretary of the Editor’s Office
Editor’s Office: 40 Lenina Prospect, Tomsk, 634050, Russia
Phone / Fax: + 7 (3822) 51-21-21 / 51-43-02