A Composite Fault Diagnosis Model for NPPs Based on Bayesian-EfficientNet Module
This work addresses fault diagnosis for nuclear power plant safety, but it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackled fault diagnosis in nuclear power plant mechanical components by proposing a composite model combining Bayesian algorithms and EfficientNet, achieving improved diagnostic accuracy with specific performance metrics reported.
This article focuses on the faults of important mechanical components such as pumps, valves, and pipelines in the reactor coolant system, main steam system, condensate system, and main feedwater system of nuclear power plants (NPPs). It proposes a composite multi-fault diagnosis model based on Bayesian algorithm and EfficientNet large model using data-driven deep learning fault diagnosis technology. The aim is to evaluate the effectiveness of automatic deep learning-based large model technology through transfer learning in nuclear power plant scenarios.