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Open set recognition fault diagnosis framework based on convolutional prototype learning network for nuclear power plants

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  • Li, Jiangkuan
  • Lin, Meng
  • Wang, Bo
  • Tian, Ruifeng
  • Tan, Sichao
  • Li, Yankai
  • Chen, Junjie

Abstract

As an Open Set Recognition (OSR) problem, nuclear power plant fault diagnosis requires not only the correct classification of known faults, but also the effective identification of unknown faults, which cannot be satisfied by most previously developed models. In this study, a novel nuclear power plant OSR fault diagnosis framework based on Convolutional Prototype Learning (CPL) is proposed, in which CPL is used to extract discriminative fault features from raw nuclear power plant data. Besides, two classification methods, One-Class Support Vector Machine (OCSVM) method and Prototype Matching by Distance (PMD) method, are developed to complete the fault diagnosis of open space for the framework. To verify the feasibility and effectiveness of the proposed OSR framework, numerical experiments on 24 OSR tasks with high-dimensional and strong-nonlinear complex nuclear power plant simulation data are conducted, and the OSR performance evaluate by normalized accuracy and Youden's index, feature visualization and convergence rates are analyzed. Results show that compared with adopting traditional Convolutional Neural Network (CNN), the proposed CPL-based framework can significantly boost the OSR diagnostic performance on nuclear power plant fault diagnosis tasks, which benefits from the excellent ability of CPL to extract intra-class compact and inter-class separable feature representation.

Suggested Citation

  • Li, Jiangkuan & Lin, Meng & Wang, Bo & Tian, Ruifeng & Tan, Sichao & Li, Yankai & Chen, Junjie, 2024. "Open set recognition fault diagnosis framework based on convolutional prototype learning network for nuclear power plants," Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:energy:v:290:y:2024:i:c:s0360544223034953
    DOI: 10.1016/j.energy.2023.130101
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    References listed on IDEAS

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    1. Li, Guolong & Li, Yanjun & Fang, Chengyue & Su, Jian & Wang, Haotong & Sun, Shengdi & Zhang, Guolei & Shi, Jianxin, 2023. "Research on fault diagnosis of supercharged boiler with limited data based on few-shot learning," Energy, Elsevier, vol. 281(C).
    2. Lin, Meng & Li, Jiangkuan & Li, Yankai & Wang, Xu & Jin, Chengyi & Chen, Junjie, 2023. "Generalization analysis and improvement of CNN-based nuclear power plant fault diagnosis model under varying power levels," Energy, Elsevier, vol. 282(C).
    3. Kara Mostefa Khelil, Chérifa & Amrouche, Badia & Benyoucef, Abou soufiane & Kara, Kamel & Chouder, Aissa, 2020. "New Intelligent Fault Diagnosis (IFD) approach for grid-connected photovoltaic systems," Energy, Elsevier, vol. 211(C).
    4. Park, Yeseul & Choi, Minsung & Choi, Gyungmin, 2022. "Fault detection of industrial large-scale gas turbine for fuel distribution characteristics in start-up procedure using artificial neural network method," Energy, Elsevier, vol. 251(C).
    5. Kapucu, Ceyhun & Cubukcu, Mete, 2021. "A supervised ensemble learning method for fault diagnosis in photovoltaic strings," Energy, Elsevier, vol. 227(C).
    6. Hassan, Syed Tauseef & Khan, Danish & Zhu, Bangzhu & Batool, Bushra, 2022. "Is public service transportation increase environmental contamination in China? The role of nuclear energy consumption and technological change," Energy, Elsevier, vol. 238(PC).
    7. Liu, Yang & Xie, Xiaoqing & Wang, Mei, 2023. "Energy structure and carbon emission: Analysis against the background of the current energy crisis in the EU," Energy, Elsevier, vol. 280(C).
    8. Zhou, Dengji & Yao, Qinbo & Wu, Hang & Ma, Shixi & Zhang, Huisheng, 2020. "Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks," Energy, Elsevier, vol. 200(C).
    9. Fan, Cheng & Lei, Yutian & Sun, Yongjun & Mo, Like, 2023. "Novel transformer-based self-supervised learning methods for improved HVAC fault diagnosis performance with limited labeled data," Energy, Elsevier, vol. 278(PB).
    10. Zhong, Fangliang & Calautit, John Kaiser & Wu, Yupeng, 2023. "Fault data seasonal imbalance and insufficiency impacts on data-driven heating, ventilation and air-conditioning fault detection and diagnosis performances for energy-efficient building operations," Energy, Elsevier, vol. 282(C).
    11. Jiang, Lulu & Deng, Zhongwei & Tang, Xiaolin & Hu, Lin & Lin, Xianke & Hu, Xiaosong, 2021. "Data-driven fault diagnosis and thermal runaway warning for battery packs using real-world vehicle data," Energy, Elsevier, vol. 234(C).
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    Cited by:

    1. 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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