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Experimental Investigation to Improve Inspection Accuracy of Magnetic Field Imaging-Based NDT Using Deep Neural Network

  • ELECTROMAGNETIC METHODS
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Abstract

A deep neural network is expected to be a useful tool to improve the accuracy of defect detection in Non-Destructive Testing (NDT). In this article, a deep neural network-based technique to improve the defect detection accuracy of an advanced NDT using imaging the distribution of time-varying magnetic flux density (hereinafter, magnetic image) was investigated. Although deep neural networks require training on large amounts of data to achieve high performance, it is not easy to obtain large amounts of useful training data from many magnetic image-based NDT sites. So, we explored a way to improve the defect detection accuracy even with a limited amount of training data by mapping the widely scattered defect information into a specific region. In this article, a deep neural network for magnetic image-based NDT was trained using transformed images in which the alternating current (AC) components of the magnetic image signal were preserved and the direct current (DC) offset values were matched to a single reference value. Here, the defect information is mainly contained in the AC components. Experiments demonstrated that the deep neural network trained using transformed images significantly improved defect detection accuracy compared to the conventional deep neural network trained on images without transformation.

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ACKNOWLEDGMENTS

This work was supported by the Korea Atomic Energy Research Institute (KAERI), granted by the Korean government (project no. 524430-22).

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Authors and Affiliations

  1. Korea Atomic Energy Research Institute, 989-111 Daedeokdaero, Yuseong-gu, 34057, Daejeon, South Korea

    Seung-Kyu Park, Jaemin Kim, Duck-Gun Park & Jonghwan Lee

  2. Department of Computer Convergence Software, Korea University, 30019, Sejong Metropolitan City, South Korea

    Minho Jo

  3. Interdisciplinary Program in IT-Bio Convergence System, Chosun University, 61452, Gwangju, South Korea

    Jinyi Lee

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  1. Seung-Kyu Park
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Correspondence to Seung-Kyu Park.

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Park, SK., Kim, J., Park, DG. et al. Experimental Investigation to Improve Inspection Accuracy of Magnetic Field Imaging-Based NDT Using Deep Neural Network. Russ J Nondestruct Test 58, 732–744 (2022). https://doi.org/10.1134/S1061830922080101

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