A Composite Fault Diagnosis Model for NPPs Based on Bayesian-EfficientNet Module

Siwei Li, Jiangwen Chen, Hua Lin, Wei Wang
11/13/2024
eess.SPcs.AIeess.SY

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Abstract

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.

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Cite this paper

@article{li2024composite,
  title  = {A Composite Fault Diagnosis Model for NPPs Based on Bayesian-EfficientNet Module},
  author = {Siwei Li and Jiangwen Chen and Hua Lin and Wei Wang},
  year   = {2024},
  eprint = {2411.17707},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2411.17707v1}
}

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