A Novel Transformer‐Based Anomaly Detection Model for the Reactor Coolant Pump in Nuclear Power Plants

Abstract

As a key equipment of the reactor coolant system, the reactor coolant pump’s operational state can have a direct impact on the coolant circulation. However, the harsh working environment can accelerate the process of equipment deterioration for the reactor coolant pump while the current alarm mechanism cannot detect the deterioration of equipment performance at the early stage. This issue may lead to anomalies developing into faults and causing unplanned shutdowns and reactor trips. In this paper, we proposed a transformer‐based anomaly detection model for reactor coolant pump condition monitoring. On the basis of retaining the time‐dependent capture ability of the original transformer network for sequence data, the proposed model has obtained a stronger learning ability of the spatial correlation between variables through the application of the attention mechanism. Historical operating data under normal conditions are used for the training process and the reconstruction errors of input signals are utilized to identify anomalies. The experimental results have indicated that the proposed model possesses stronger feature learning capabilities, evidenced by improved performance in signal reconstruction and anomaly detection, which can help to detect the abnormal status at an earlier stage.