Towards Open-Set Fault Diagnosis for Reactor Coolant Pumps under Unknown Fault Conditions

The reactor coolant pump – vibration monitoring system (RCP-VMS) ensures the safe operation of nuclear power plants by detecting anomalies in the shaft and bearing components of reactor coolant pumps. While effective for known fault modes, conventional AI-based diagnostic models often fail to detect unseen faults, especially when labeled data are limited. To address this limitation, an open-set recognition approach is proposed based on class-specific semantic reconstruction. Vibration signals collected from RCP-VMS are processed into orbit plot and recurrence plots, which serve as multi-channel image inputs to the model. The reconstruction errors are then used to distinguish both known and unknown fault conditions. Experimental results demonstrate that the proposed method achieves competitive closed-set accuracy while significantly enhancing open-set fault detection performance compared to baseline models. This approach enhances the reliability and robustness of fault diagnosis in safety-critical rotating machinery such as RCPs.