IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v369y2024ics0306261924009863.html
   My bibliography  Save this article

Open set compound fault recognition method for nuclear power plant based on label mask weighted prototype learning

Author

Listed:
  • Zhou, Shiqi
  • Lin, Meng
  • Huang, Shilong
  • Xiao, Kai

Abstract

Most of the existing data-driven methods for diagnosing faults in nuclear power plants (NPP) concentrate on addressing single fault problems under the closed set hypothesis. However, the practical application of these methods is severely limited by unknown faults and compound faults caused by the complex operation characteristics of NPP. In order to extend the NPP fault diagnosis model to the Open Set Compound Fault Recognition (OSCFR), a method for OSCFR based on residual capsule network and label mask weighted prototype learning (Mask-CCPN) is proposed. In this method, the residual capsule network is constructed to achieve the decoupled classification of compound faults. Additionally, the label mask weighted method is used to enhance the existing prototype learning, which using the prototype information from single faults to identify the compound faults. Numerical experiments on complex NPP simulation data demonstrate that the proposed method can effectively solve the OSCFR of NPP without requiring additional storage space. The test results under a single fault data set are superior to the state-of-the-art method. In addition, this study confirms that the uniform distribution hypothesis is more suitable for the decision-making process of prototype learning, while the Gaussian distribution hypothesis and Weibull distribution hypothesis have poor results. Overall, the proposed method is promising to expand the application range of NPP fault diagnosis to various types of faults.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:369:y:2024:i:c:s0306261924009863
    DOI: 10.1016/j.apenergy.2024.123603
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924009863
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.123603?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Song, Houde & Song, Meiqi & Liu, Xiaojing, 2022. "Online autonomous calibration of digital twins using machine learning with application to nuclear power plants," Applied Energy, Elsevier, vol. 326(C).
    2. Zhao, Chengxuan & Yang, Xiao & Yu, Jie & Yang, Minghan & Wang, Jianye & Chen, Shuai, 2023. "Interval type-2 fuzzy logic control for a space nuclear reactor core power system," Energy, Elsevier, vol. 280(C).
    3. Yang, Jaemin & Kim, Jonghyun, 2020. "Accident diagnosis algorithm with untrained accident identification during power-increasing operation," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    4. Gong, Bin & An, Aimin & Shi, Yaoke & Zhang, Xuemin, 2024. "Fast fault detection method for photovoltaic arrays with adaptive deep multiscale feature enhancement," Applied Energy, Elsevier, vol. 353(PA).
    5. Li, Bingxu & Cheng, Fanyong & Zhang, Xin & Cui, Can & Cai, Wenjian, 2021. "A novel semi-supervised data-driven method for chiller fault diagnosis with unlabeled data," Applied Energy, Elsevier, vol. 285(C).
    6. Hong, Sanghyun & Bradshaw, Corey J.A. & Brook, Barry W., 2014. "Nuclear power can reduce emissions and maintain a strong economy: Rating Australia’s optimal future electricity-generation mix by technologies and policies," Applied Energy, Elsevier, vol. 136(C), pages 712-725.
    7. Yang, Shuai & Yuan, Jun & Nian, Victor & Li, Lu & Li, Hailong, 2022. "Economics of marinised offshore charging stations for electrifying the maritime sector," Applied Energy, Elsevier, vol. 322(C).
    8. Wang, Pengfei & Zhang, Jiaxuan & Wan, Jiashuang & Wu, Shifa, 2022. "A fault diagnosis method for small pressurized water reactors based on long short-term memory networks," Energy, Elsevier, vol. 239(PC).
    9. 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).
    10. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    11. Zhang, Yuanshi & Qian, Wenyan & Ye, Yujian & Li, Yang & Tang, Yi & Long, Yu & Duan, Meimei, 2023. "A novel non-intrusive load monitoring method based on ResNet-seq2seq networks for energy disaggregation of distributed energy resources integrated with residential houses," Applied Energy, Elsevier, vol. 349(C).
    12. Zhang, Qingang & Zeng, Wei & Lin, Qinjie & Chng, Chin-Boon & Chui, Chee-Kong & Lee, Poh-Seng, 2023. "Deep reinforcement learning towards real-world dynamic thermal management of data centers," Applied Energy, Elsevier, vol. 333(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    2. Jinrui Nan & Bo Deng & Wanke Cao & Jianjun Hu & Yuhua Chang & Yili Cai & Zhiwei Zhong, 2022. "Big Data-Based Early Fault Warning of Batteries Combining Short-Text Mining and Grey Correlation," Energies, MDPI, vol. 15(15), pages 1-19, July.
    3. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
    4. Nock, Destenie & Baker, Erin, 2019. "Holistic multi-criteria decision analysis evaluation of sustainable electric generation portfolios: New England case study," Applied Energy, Elsevier, vol. 242(C), pages 655-673.
    5. Silverio HERNANDEZ-MORENO, 2019. "International Experiences On The Implementation Of Public Policies For Urban Planning To Face Climate Change," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 14(2), pages 72-88, May.
    6. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    7. Qiu, Yu & Xu, Yucong & Li, Qing & Wang, Jikang & Wang, Qiliang & Liu, Bin, 2021. "Efficiency enhancement of a solar trough collector by combining solar and hot mirrors," Applied Energy, Elsevier, vol. 299(C).
    8. Fan, Siyuan & Wang, Yu & Cao, Shengxian & Zhao, Bo & Sun, Tianyi & Liu, Peng, 2022. "A deep residual neural network identification method for uneven dust accumulation on photovoltaic (PV) panels," Energy, Elsevier, vol. 239(PD).
    9. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    10. Wang, Yu & Gu, Jibao & Wu, Jianlin, 2020. "Explaining local residents’ acceptance of rebuilding nuclear power plants: The roles of perceived general benefit and perceived local benefit," Energy Policy, Elsevier, vol. 140(C).
    11. Amin, Amin & Mourshed, Monjur, 2024. "Community stochastic domestic electricity forecasting," Applied Energy, Elsevier, vol. 355(C).
    12. Ding, Dong & Li, Junhuai & Wang, Huaijun & Wang, Kan & Feng, Jie & Xiao, Ming, 2024. "ApplianceFilter: Targeted electrical appliance disaggregation with prior knowledge fusion," Applied Energy, Elsevier, vol. 365(C).
    13. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    14. Chao Ding & Nan Zhou, 2020. "Using Residential and Office Building Archetypes for Energy Efficiency Building Solutions in an Urban Scale: A China Case Study," Energies, MDPI, vol. 13(12), pages 1-16, June.
    15. Zhe Dong & Zhonghua Cheng & Yunlong Zhu & Xiaojin Huang & Yujie Dong & Zuoyi Zhang, 2023. "Review on the Recent Progress in Nuclear Plant Dynamical Modeling and Control," Energies, MDPI, vol. 16(3), pages 1-19, February.
    16. Chévez, Pedro Joaquín & Martini, Irene & Discoli, Carlos, 2019. "Methodology developed for the construction of an urban-energy diagnosis aimed to assess alternative scenarios: An intra-urban approach to foster cities’ sustainability," Applied Energy, Elsevier, vol. 237(C), pages 751-778.
    17. Han, Ouzhu & Ding, Tao & Yang, Miao & Jia, Wenhao & He, Xinran & Ma, Zhoujun, 2024. "A novel 4-level joint optimal dispatch for demand response of data centers with district autonomy realization," Applied Energy, Elsevier, vol. 358(C).
    18. Corey J. A. Bradshaw & Barry W. Brook, 2016. "Implications of Australia's Population Policy for Future Greenhouse Gas Emissions Targets," Asia and the Pacific Policy Studies, Wiley Blackwell, vol. 3(2), pages 249-265, May.
    19. Alhamwi, Alaa & Medjroubi, Wided & Vogt, Thomas & Agert, Carsten, 2019. "Development of a GIS-based platform for the allocation and optimisation of distributed storage in urban energy systems," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    20. Fan, Cheng & Chen, Ruikun & Mo, Jinhan & Liao, Longhui, 2024. "Personalized federated learning for cross-building energy knowledge sharing: Cost-effective strategies and model architectures," Applied Energy, Elsevier, vol. 362(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:369:y:2024:i:c:s0306261924009863. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.