IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v316y2025ics0360544225001495.html
   My bibliography  Save this article

Fault diagnosis for supercharged boiler based on self-improving few-shot learning

Author

Listed:
  • Li, Guolong
  • Li, Yanjun
  • Su, Jian
  • Wang, Haotong
  • Sun, Shengdi
  • Zhao, Jiarui
  • Zhang, Guolei
  • Shi, Jianxin

Abstract

This study proposes a fault diagnosis method, SNN-MSU(H), to address the issue that the diagnosis model and support set cannot be updated and improved during the supercharged boiler fault diagnosis process. The proposed method sends samples that are not correctly diagnosed by the model to experts for re-diagnosis. These expert-re-diagnosed samples are then collected for model re-training. The results show that as the number of test samples increases or noise decreases, the model's accuracy increases and the need for expert intervention decreases. The SNN-MSU's accuracy is greater than 95 %, and the number of expert interventions in every 10 test samples does not exceed 1 when there are no samples in the initial support set. When the model is pre-trained with a few supercharged boiler fault samples, the SNN-MSU's accuracy exceeds 97 %, and that of SNN-MSUH exceeds 99 %. Finally, actual system tests demonstrate that SNN-MSU(H) can accurately detect new fault categories, such as valve disturbances, and request expert intervention in time. After expert intervention, the accuracy of the model diagnosis can be improved based on the knowledge provided by the experts.

Suggested Citation

  • Li, Guolong & Li, Yanjun & Su, Jian & Wang, Haotong & Sun, Shengdi & Zhao, Jiarui & Zhang, Guolei & Shi, Jianxin, 2025. "Fault diagnosis for supercharged boiler based on self-improving few-shot learning," Energy, Elsevier, vol. 316(C).
    • Handle: RePEc:eee:energy:v:316:y:2025:i:c:s0360544225001495
    DOI: 10.1016/j.energy.2025.134507
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225001495
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.134507?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

    for a different version of it.

    References listed on IDEAS

    as
    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. Dan Ling & Chaosong Li & Yan Wang & Pengye Zhang, 2022. "Fault Detection and Identification of Furnace Negative Pressure System with CVA and GA-XGBoost," Energies, MDPI, vol. 15(17), pages 1-19, August.
    3. Indrawan, Natarianto & Shadle, Lawrence J. & Breault, Ronald W. & Panday, Rupendranath & Chitnis, Umesh K., 2021. "Data analytics for leak detection in a subcritical boiler," Energy, Elsevier, vol. 220(C).
    4. Dongliang Li & Shaojun Xia & Jianghua Geng & Fankai Meng & Yutao Chen & Guoqing Zhu, 2022. "Discriminability Analysis of Characterization Parameters in Micro-Leakage of Turbocharged Boiler’s Evaporation Tube," Energies, MDPI, vol. 15(22), pages 1-20, November.
    5. Salman Khalid & Hyunho Hwang & Heung Soo Kim, 2021. "Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant," Mathematics, MDPI, vol. 9(21), pages 1-27, November.
    6. Fan, Cheng & Wu, Qiuting & Zhao, Yang & Mo, Like, 2024. "Integrating active learning and semi-supervised learning for improved data-driven HVAC fault diagnosis performance," Applied Energy, Elsevier, vol. 356(C).
    7. Trojan, Marcin, 2019. "Modeling of a steam boiler operation using the boiler nonlinear mathematical model," Energy, Elsevier, vol. 175(C), pages 1194-1208.
    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. 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. Li, Guolong & Li, Yanjun & Li, Site & Sun, Shengdi & Wang, Haotong & Su, Jian & Shi, Jianxin & Zhou, Xin, 2025. "Research on anomaly detection of steam power system based on the coupling of thermoeconomics and autoencoder," Energy, Elsevier, vol. 318(C).
    3. Bi, Yubo & Wu, Qiulan & Wang, Shilu & Shi, Jihao & Cong, Haiyong & Ye, Lili & Gao, Wei & Bi, Mingshu, 2023. "Hydrogen leakage location prediction at hydrogen refueling stations based on deep learning," Energy, Elsevier, vol. 284(C).
    4. Majdak, Marek & Grądziel, Sławomir, 2020. "Influence of thermal and flow conditions on the thermal stresses distribution in the evaporator tubes," Energy, Elsevier, vol. 209(C).
    5. Pei Li & Ting Bao & Jian Guan & Zifu Shi & Zengxiao Xie & Yonggang Zhou & Wei Zhong, 2023. "Computational Analysis of Tube Wall Temperature of Superheater in 1000 MW Ultra-Supercritical Boiler Based on the Inlet Thermal Deviation," Energies, MDPI, vol. 16(3), pages 1-15, February.
    6. 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).
    7. Prashant Kumar & Salman Khalid & Heung Soo Kim, 2023. "Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review," Mathematics, MDPI, vol. 11(13), pages 1-37, July.
    8. Taler, Jan & Trojan, Marcin & Dzierwa, Piotr & Kaczmarski, Karol & Węglowski, Bohdan & Taler, Dawid & Zima, Wiesław & Grądziel, Sławomir & Ocłoń, Paweł & Sobota, Tomasz & Rerak, Monika & Jaremkiewicz,, 2023. "The flexible boiler operation in a wide range of load changes with considering the strength and environmental restrictions," Energy, Elsevier, vol. 263(PB).
    9. Jia, Xiongjie & Sang, Yichen & Li, Yanjun & Du, Wei & Zhang, Guolei, 2022. "Short-term forecasting for supercharged boiler safety performance based on advanced data-driven modelling framework," Energy, Elsevier, vol. 239(PE).
    10. Trojan, Marcin & Taler, Jan & Smaza, Krzysztof & Wróbel, Wojciech & Dzierwa, Piotr & Taler, Dawid & Kaczmarski, Karol, 2022. "A new software program for monitoring the energy distribution in a thermal waste treatment plant system," Renewable Energy, Elsevier, vol. 184(C), pages 1055-1073.
    11. Tucker, Swatara & Indrawan, Natarianto & Shadle, Lawrence J. & Harun, Nor Farida & Tucker, David, 2024. "Performance degradation in an advanced power system by analyzing process dynamics," Applied Energy, Elsevier, vol. 369(C).
    12. Wójtowicz-Wróbel, Agnieszka & Kania, Olga & Kocewiak, Katarzyna & Wójtowicz, Ryszard & Dzierwa, Piotr & Trojan, Marcin, 2023. "Thermal-flow calculations for a thermal waste treatment plant and CFD modelling of the spread of gases in the context of urban structures," Energy, Elsevier, vol. 263(PD).
    13. Laubscher, Ryno, 2019. "Time-series forecasting of coal-fired power plant reheater metal temperatures using encoder-decoder recurrent neural networks," Energy, Elsevier, vol. 189(C).
    14. Nawaz, Zanib & Ali, Usman, 2020. "Techno-economic evaluation of different operating scenarios for indigenous and imported coal blends and biomass co-firing on supercritical coal fired power plant performance," Energy, Elsevier, vol. 212(C).
    15. Jie Liu & Han Cheng & Qingkuan Liu & Hailong Wang & Jianqing Bu, 2023. "Research on the Damage Diagnosis Model Algorithm of Cable-Stayed Bridges Based on Data Mining," Sustainability, MDPI, vol. 15(3), pages 1-15, January.
    16. Trojan, Marcin & Wielgus, Szymon & Taler, Jan & Dzierwa, Piotr & Kaczmarski, Karol & Granda, Mariusz, 2025. "Optimization of the operation of circulation pumps in the wet flue gas desulfurization system," Energy, Elsevier, vol. 325(C).
    17. Chantasiriwan, Somchart, 2021. "Optimum installation of flue gas dryer and additional air heater to increase the efficiency of coal-fired utility boiler," Energy, Elsevier, vol. 221(C).
    18. Granda, Mariusz & Trojan, Marcin & Taler, Dawid, 2020. "CFD analysis of steam superheater operation in steady and transient state," Energy, Elsevier, vol. 199(C).
    19. Konstantin Osintsev & Sergei Aliukov & Sulpan Kuskarbekova, 2021. "Development of Methodological Bases of the Processes of Steam Formation in Coil Type Boilers Using Solar Concentrators," Energies, MDPI, vol. 14(8), pages 1-22, April.
    20. Zhang, Jiahao & Peng, Ruo & Lu, Chenbei & Wu, Chenye, 2025. "Computationally efficient data synthesis for AC-OPF: Integrating Physics-Informed Neural Network solvers and active learning," Applied Energy, Elsevier, vol. 378(PA).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:energy:v:316:y:2025:i:c:s0360544225001495. 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.journals.elsevier.com/energy .

    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.