Cross-operating-condition fault diagnosis of a small module reactor based on CNN-LSTM transfer learning with limited data

Fault samples of Small Modular Reactor (SMR) are scarce and the data distribution cross operating conditions are different, resulting in poor generalization performance of fault diagnosis model. In this paper, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are integrated and a novel CNN-LSTM transfer learning method is proposed for fault diagnosis of SMR at different power levels, which is used to improve the deep extraction capability of spatial and temporal features of fault data. Based on small sample data and feature visualization technology, five different feature transfer strategies are compared to obtain the best generalization performance of the cross-operating condition fault diagnosis model. In addition, the effects of different network structures and hyperparameter combination settings of the CNN-LSTM model on the convergence speed and accuracy of transfer learning are analyzed to optimize the design of SMR fault diagnosis model. The results show that the CNN-LSTM method has stronger fault feature extraction capability than other deep learning methods. The simulation results also show that the proposed transfer learning strategy and the optimized network structure and hyperparameters could significantly enhance the generalization performance and accuracy of SMR fault diagnosis under different operating conditions with few labeled samples.