Anomaly detection in sealed spent nuclear fuel canisters using unsupervised machine learning

Abstract

After years of operation in reactors at nuclear power plants, nuclear fuel assemblies (FA) become high-level radioactive waste known as spent nuclear fuel (SNF). In the absence of a permanent disposal solution, SNF is stored in sealed stainless-steel dry storage canisters. Damage to the FA can occur during handling, storage, or transportation, and detecting such damage is critical prior to a long-term disposal. However, direct visual inspection is costly and sometimes not feasible because the canisters are sealed, necessitating non-destructive evaluation techniques for their internal condition assessment. In this study, a 2/3-scale physical canister mock-up with mock-up FA was employed to simulate multiple FA damage modes. Experimental modal analysis was performed to obtain frequency response functions (FRF) from the exterior surface of the canister bottom plate. A variational autoencoder (VAE) was trained exclusively on FRF data from the undamaged canister to learn the response of the healthy structure. FRF from the canister with FA damage were then processed through the trained VAE to generate reconstruction error signals. These signals were analyzed using unsupervised machine learning models, including isolation forest and local outlier factor, to detect anomalies. Additionally, a probabilistic approach was developed by fitting a Gaussian mixture model (GMM) to the latent space of the trained VAE. The resulting anomaly scores showed strong separation between healthy and damaged samples, with higher scores corresponding to greater damage severity. The GMM-based method achieved an F1 score of 0.998 on the testing dataset. These results demonstrate that the VAE-based framework can effectively detect FA damage within sealed canisters using unlabeled FRF data. This offers a practical solution for detecting anomalous behavior in real-world SNF canister inspections.