Research on a strongly generalizable fault diagnosis method based on adversarial transfer learning
Chenlong Dong
IntroductionShallow 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.MethodsA cross-reactor-type fault diagnosis method based on adversarial transfer learning is proposed. By