The application discloses a
nuclear power plant fault diagnosis and maintenance decision-making method combining transfer learning and expert experience, relates to the technical fields of
nuclear power plant operation and maintenance,
equipment monitoring and fault diagnosis, and comprises the following steps: collecting equipment fault
simulation data,
unit operation data and expert maintenance experience data, constructing a transfer learning model after
feature extraction and normalization
processing; training a feature extractor and a
label classifier based on the
simulation data, reducing the feature distribution difference between the source domain and the target domain through domain adversarial
adaptation, and fine-tuning the model in combination with actual data to improve the
verification accuracy; building an expert
knowledge base, using the model to diagnose actual data and calculating the confidence, automatically triggering the
knowledge base maintenance scheme matching or manual
review process; dynamically updating the
knowledge base in combination with the review result and new cases, encapsulating the model into an API and integrating it into the existing monitoring platform of the
nuclear power plant to realize real-
time data access, diagnosis result output and function testing, and the application improves the fault diagnosis efficiency and the scientific nature of the maintenance decision-making.