Research on Frequency-Domain Learning-Based Fault Diagnosis Methods in Nuclear Power Plants via a Lightweight Complex-Valued Neural Network

Nuclear power plant (NPP) fault diagnosis is critical to ensure the safe, stable and economic operation of nuclear facilities. Existing deep learning-based NPP fault diagnosis methods primarily extract features from the temporal and spatial domain. Given recent advances in frequency-domain learning and lightweight models for time-series modeling, this paper proposes a frequency-domain learning-based fault diagnosis method for multi-modular high-temperature gas-cooled reactor (mHTGR) modules. It extracts temporal and spatial frequency-domain features using a lightweight complex-valued neural network (CVNN), which are then applied for fault classification and severity estimation. Under typical fault detection tasks for an mHTGR, comparative experiments verify that the proposed method outperforms existing methods that utilize long short-term memory (LSTM) and graph neural networks (GNNs) for spatiotemporal feature extraction in terms of classification accuracy, mean squared error (MSE) loss, and inference time.