Hierarchical FFT-LSTM-GCN based model for nuclear power plant fault diagnosis considering spatio-temporal features fusion

As safety-critical infrastructure, nuclear power plants (NPPs) require enhanced safety measures and risk minimization. To achieve this goal and to aid operator decision-making while reducing human error, various fault diagnosis (FD) methods have been proposed. Among these, deep learning-based approaches have demonstrated significant success in FD because of their ability to effectively extract information about machine degradation. However, most existing methods primarily focus on temporal features while neglecting spatial features. To leverage both temporal and spatial features effectively and achieve high diagnostic accuracy, we propose a hierarchical deep learning based model that comprises the fast Fourier transform (FFT), long short-term memory networks (LSTM) and graph convolutional networks (GCN). The application of FFT to sensor sequences effectively mitigates their volatility. The use of GCN enables automated extraction of intricate spatial features from multi-sensor data, while LSTM is adept at directly extracting temporal features from historical input data. To validate the proposed model, we conducted three experiments using data simulated by the personal computer transient analyzer (PCTRAN), and the results demonstrate that the diagnostic accuracy of the proposed hierarchical FFT-LSTM-GCN model surpasses that of any single model for NPP FD.