A Composite Fault Diagnosis Model for NPPs Based on Bayesian-EfficientNet Module
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916496988438528 |
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| author | Li, Siwei Chen, Jiangwen Lin, Hua Wang, Wei |
| author_facet | Li, Siwei Chen, Jiangwen Lin, Hua Wang, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17707 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Composite Fault Diagnosis Model for NPPs Based on Bayesian-EfficientNet Module Li, Siwei Chen, Jiangwen Lin, Hua Wang, Wei Signal Processing Artificial Intelligence Systems and Control 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. |
| title | A Composite Fault Diagnosis Model for NPPs Based on Bayesian-EfficientNet Module |
| topic | Signal Processing Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2411.17707 |