Automated detection of corrosion in used nuclear fuel dry storage canisters using residual neural networks

Abstract

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 pitting and stress corrosion cracking, with a focus on dry storage 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 cracks 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.

Review of Prediction of Stress Corrosion Cracking in Gas Pipelines Using Machine Learning

“…Papamarkou et al [158] used convolution and residual neural networks to forecast the development of SCC (RNN). Their research demonstrates how online real-time measurements can be used to forecast SCC and pitting corrosion for usage in dry storage canisters for nuclear fuel.…”