Latent Fault Detection and Diagnosis for Control Rods Drive Mechanisms in Nuclear Power Reactor Base

Latent fault detection and diagnosis (LFDD) for equipment are crucial for safety and reliability in nuclear power reactors (NPRs). In this article, a high accuracy LFDD method combining gate recurrent unit-based autoencoder (GRU-AE) and random forest (RF) was proposed for control rod drive mechanisms (CRDM) in pressurized water reactors (PWRs). The movement sequences of the CRDM coils’ current reflecting the degradation of CRDM were assumed as time series and taken by the sliding window. GRU-AE, a network containing an encoder and decoder, was applied to build the health operating data reconstruction model. The labeled abnormal data were generated by adding the typical current waveforms with a Gaussian noise sequence. The latent fault could be detected by feeding online datasets into the well-trained model because of reconstruction error sensitivity to a slight/minor deflection. And then, RF was used as a classifier to diagnose the types of abnormalities. The results demonstrate that combining the GRU-AE and RF can realize high-accuracy detection on real-world imbalanced datasets.