  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.
