Transfer learning with limited labeled data for fault diagnosis in nuclear power plants

Numerous achievements have been made in intelligent fault diagnosis of nuclear power plants, under the assumption that the labeled training data is sufficient and in the same distribution as the test data, which is always contrary to actual situations and limits their practical applications. Therefore, a novel transfer learning framework with diverse transfer strategies is proposed on basis of a pre-trained CNN model in this study, to deal with the problem of limited labeled data in target tasks. The pre-trained CNN is obtained by ample data in source task, then its weights are transferred to the target models and fine-tuned. The results show that with proper transfer strategy, the proposed transfer learning framework significantly boosts the diagnostic performances, compared with training a brand-new CNN model from scratch with insufficient labeled target data. Conse-quently, the feasibility and superiority of transfer learning in nuclear power plant fault diagnosis with limited labeled data are proven.