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<p>The Fukushima nuclear accident was caused by the excessive reactivity of nuclear fuel rods, prompting recent efforts to reduce fuel reactivity through the application of chromium (Cr) coatings. Maintaining a consistent Cr coating thickness is essential to ensure nuclear safety, which requires accurate measurement methods. Although various techniques have been proposed for measuring Cr coating thickness, explaining the relationship between measured signals and the actual physical thickness remains a significant challenge. Ideally, acquiring large-scale datasets would enable the discovery of such physical relationships, but this is difficult in practice. To address this issue, this paper proposes an approach that leverages data augmentation and artificial intelligence (AI) methods to enable accurate thickness prediction using limited data from ECT time-series signals. In particular, a predictive model based on a multi-head attention long short-term memory (LSTM) architecture was developed, and multiple time-series data augmentation methods were applied to enhance performance despite data scarcity. The results demonstrate that the proposed method not only improves measurement reliability in the context of nuclear safety, but also provides a practical framework for guiding sensor development in other fields through the integration of AI and data augmentation.</p>

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Enhancing chromium coating thickness estimation with multi-head attention LSTM and data augmentation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月11日

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  • Minsu Jeon,
  • Wonjae Choi,
  • Jeong Won Park,
  • Jaebeom Lee,
  • Duhwan Mun

摘要

The Fukushima nuclear accident was caused by the excessive reactivity of nuclear fuel rods, prompting recent efforts to reduce fuel reactivity through the application of chromium (Cr) coatings. Maintaining a consistent Cr coating thickness is essential to ensure nuclear safety, which requires accurate measurement methods. Although various techniques have been proposed for measuring Cr coating thickness, explaining the relationship between measured signals and the actual physical thickness remains a significant challenge. Ideally, acquiring large-scale datasets would enable the discovery of such physical relationships, but this is difficult in practice. To address this issue, this paper proposes an approach that leverages data augmentation and artificial intelligence (AI) methods to enable accurate thickness prediction using limited data from ECT time-series signals. In particular, a predictive model based on a multi-head attention long short-term memory (LSTM) architecture was developed, and multiple time-series data augmentation methods were applied to enhance performance despite data scarcity. The results demonstrate that the proposed method not only improves measurement reliability in the context of nuclear safety, but also provides a practical framework for guiding sensor development in other fields through the integration of AI and data augmentation.