Fault identification and signal restoration of sensors based on artificial intelligence at nuclear power plants

In nuclear power plants (NPPs), the reliability of sensor signals is important for operators’ situational awareness and for ensuring safe operation. Operators make decisions based on information collected from various instrumentation sensors, which serve as inputs for artificial intelligence (AI)-based operator support systems. However, signal faults caused by sensor malfunctions, aging, and environmental factors can occur in actual operating environments. These faults may delay accident recognition or cause misdiagnosis, increasing human error risk. Signal integrity is particularly important in emergency situations, where rapid decision-making is imperative. This study proposes an AI-based algorithm for effective identification and restoration of sensor signal faults during emergencies in NPPs. First, the algorithm verifies the input signals to detect faults. Subsequently, it selectively restores only faulty signals. The restored signals are then used for accident diagnosis, preventing performance degradation caused by faulty inputs. The algorithm was evaluated using artificially generated data for three types of faults: bias, drift, and stuck. Results demonstrated high accuracy in fault detection and restoration. Additionally, restored signals enabled accurate accident classification. This study is expected to enhance NPP safety and reliability by mitigating the impact of signal faults on AI-based operator support systems and decision-making.