Research on a strongly generalizable fault diagnosis method based on adversarial transfer learning

Introduction Shallow machine learning algorithms exhibit low efficiency in fault diagnosis under the conditions of small-sample and unlabeled data. To address this critical problem, this paper focuses on developing an effective fault diagnosis method suitable for cross-reactor-type scenarios, which is of great significance for improving the safety and operational level of nuclear power plants. Methods A cross-reactor-type fault diagnosis method based on adversarial transfer learning is proposed. By integrating deep learning and transfer learning techniques, a hybrid domain-adversarial learning model is constructed. The overall loss function of the model is designed to effectively extract transferable features between related reactor types, and corresponding validation experiments are carried out to verify the model's feasibility and effectiveness. Results The experimental validation shows that the proposed hybrid domain-adversarial learning model can effectively extract transferable features across different reactor types, which solves the problem of low efficiency of shallow machine learning algorithms in fault diagnosis under small-sample and unlabeled data conditions. The model achieves reliable fault diagnosis performance in cross-reactor-type scenarios. Discussion When applied to cross-reactor-type nuclear power plant fault diagnosis, the research findings can significantly enhance the safety of nuclear power plants, improve their economic performance and operational efficiency. Furthermore, this research effectively promotes the intelligence level and autonomous decision-making capabilities of nuclear power plants, providing a valuable technical reference for the intelligent development of the nuclear power industry.