Online Diversion Detection in Nuclear Fuel Cycles via Multimodal Observations

In nuclear fuel cycles, an enrichment facility typically provides low enriched uranium (LEU) to a number of customers. We consider monitoring an enrichment facility to timely detect a possible diversion of highly enriched uranium (HEU). To increase the the detection accuracy it is important to efficiently use the available information diversity. In this work, it is assumed that the shipment times and the power consumption of the enrichment facility are observed for each shipment of enriched uranium. We propose to initially learn the statistical patterns of the enrichment facility through the bimodal observations in a training period, that is known to be free of diversions. Then, for the goal of timely diversion detection, we propose to use an online detection algorithm which sequentially compares each set of new observations in the test period, which possibly includes diversions, to the learned patterns, and raises a diversion alarm when a significant statistical deviation is detected. The efficacy of the proposed method is shown by comparing its detection performance to those of the traditional detection methods in the Statistics literature.