用残差神经网络自动检测用过的核燃料干式贮存罐中的腐蚀
Automated detection of corrosion in used nuclear fuel dry storage canisters using residual neural networks
摘要：
无损评估方法在确保许多行业的组件完整性和安全性方面发挥着重要作用。操作员疲劳可能在这种方法的可靠性中起关键作用。这对于检查高价值资产或具有高故障后果的资产（例如航空航天和核部件）非常重要。卷积神经网络的最新进展可以支持和自动化这些检查工作。本文提出了使用残差神经网络（ResNets）的实时检测的腐蚀，包括氧化铁变色，点蚀和应力腐蚀开裂，在干存储不锈钢罐住房用核燃料。所提出的方法将核罐图像裁剪成较小的瓦片，在这些瓦片上训练ResNet，并使用ResNet预测为腐蚀的瓦片的每图像计数将图像分类为腐蚀或完好。结果表明，这种深度学习方法可以通过较小的瓷砖检测腐蚀的位置，同时可以高精度地推断图像是否来自腐蚀的罐。因此，所提出的方法有望自动化和加速核燃料罐检查，以最大限度地减少检查成本，并部分取代人工进行的现场检查，从而减少人员的辐射剂量。
英文摘要：
Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of corrosion, including iron oxide discoloration, pitting and stress corrosion cracking, in dry storage stainless steel canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
