Development of a Time-Series Forecasting Model for Detecting Anomalies in Nuclear Reactor Data

Anomaly detection systems identify abnormal behaviors, and can increase the uptime, safety, and profitability of an industrial system. This research investigates the development of an AI model for detecting anomalies in nuclear reactors. An LSTM network was used to predict the value of a key reactor signal, and then the predictions are compared to the measured values in order to determine if the data is abnormal. The predictive AI model was trained using regular operation data from the nuclear reactor at Purdue University, PUR-1. It is shown in the experiment that the model can accurately track reactor neutron counts during normal operation, with an average error of less than 5% when predicting five seconds into the future. It is also shown that the model reacts to abnormal inputs, with average errors above 50% when fed data which simulates a false data injection cyberattack. The framework of using prediction error to identify anomalies is investigated and a false positive rate of 0.2% is achieved on the normal evaluation dataset while still identifying the abnormal data as anomalous.