Open set recognition fault diagnosis framework based on convolutional prototype learning network for nuclear power plants
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DOI: 10.1016/j.energy.2023.130101
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- Wang, Haotong & Shi, Jianxin & Lin, Chaojing & Liu, Xinmeng & Li, Guolong & Sun, Shengdi & Zhou, Xin & Li, Yanjun, 2025. "Nuclear power systems unsupervised anomaly localization considering spatiotemporal information and influence mechanism between devices," Energy, Elsevier, vol. 325(C).
- Jiang, Dingyu & Wu, Hexin & Gou, Junli & Zhang, Bo & Shan, Jianqiang, 2025. "Performance analysis and improvement of data-driven fault diagnosis models under domain discrepancy base on a small modular reactor," Energy, Elsevier, vol. 316(C).
- Furlong, Aidan & Alsafadi, Farah & Palmtag, Scott & Godfrey, Andrew & Wu, Xu, 2025. "Data-driven prediction and uncertainty quantification of PWR crud-induced power shift using convolutional neural networks," Energy, Elsevier, vol. 316(C).
- Zhou, Shiqi & Lin, Meng & Huang, Shilong & Xiao, Kai, 2024. "Open set compound fault recognition method for nuclear power plant based on label mask weighted prototype learning," Applied Energy, Elsevier, vol. 369(C).
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