IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v313y2024ics0360544224035527.html
   My bibliography  Save this article

The real-time detection of defects in nuclear power pipeline thermal insulation glass fiber by deep-learning

Author

Listed:
  • Zheng, Qiankang
  • Lu, Le
  • Chen, Zhaofeng
  • Wu, Qiong
  • Yang, Mengmeng
  • Hou, Bin
  • Chen, Shijie
  • Zhang, Zhuoke
  • Yang, Lixia
  • Cui, Sheng

Abstract

Glass fiber, prized for its high-temperature thermal insulation and radiation resistance, serves as a crucial material for insulating nuclear power pipelines. However, the harsh operational conditions often lead to material defects, underscoring the importance of defect detection for energy efficiency and personnel safety, and manually segmenting and classifying defects can be time-consuming and increase risks. Hence, there is a pressing need for a real-time and accurate detection method. In this work, infrared images of nuclear power pipeline thermal insulation glass fiber defects were collected to establish the dataset, and the damage mechanisms were analyzed. Besides, various prevalent object detection models were tested and found that YOLOv8n exhibited significant potential for improvement with exceptional speed performance and detection accuracy. Through integrated EMA attention blocks, incorporating the FasterNet blocks into the backbone, retrofitting the neck layers with the slim-neck structure, and implementing DyHead in the YOLOv8n's head, our improved model achieves the highest values of mean Average Precision (mAP) scores with 0.5:0.95 intersection over union (IoU) of 57.6 %, and 0.5 IoU of 86.8 %, while maintaining the original high detection speed and low number of parameters, ensures suitability for real-time detection deployment on edge devices of nuclear power plants.

Suggested Citation

  • Zheng, Qiankang & Lu, Le & Chen, Zhaofeng & Wu, Qiong & Yang, Mengmeng & Hou, Bin & Chen, Shijie & Zhang, Zhuoke & Yang, Lixia & Cui, Sheng, 2024. "The real-time detection of defects in nuclear power pipeline thermal insulation glass fiber by deep-learning," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224035527
    DOI: 10.1016/j.energy.2024.133774
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224035527
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.133774?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. Zhuo Jiang & Yingjie Wu & Han Zhang & Lixun Liu & Jiong Guo & Fu Li, 2023. "A Modified JFNK for Solving the HTR Steady State Secondary Circuit Problem," Energies, MDPI, vol. 16(5), pages 1-14, February.
    2. Zhang, Tianhao & Dong, Zhe & Huang, Xiaojin, 2024. "Multi-objective optimization of thermal power and outlet steam temperature for a nuclear steam supply system with deep reinforcement learning," Energy, Elsevier, vol. 286(C).
    3. Cui, Chengcheng & Zhang, Junli & Shen, Jiong, 2023. "System-level modeling, analysis and coordinated control design for the pressurized water reactor nuclear power system," Energy, Elsevier, vol. 283(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).

    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. Zhang, Tianhao & Dong, Zhe & Huang, Xiaojin, 2024. "Multi-objective optimization of thermal power and outlet steam temperature for a nuclear steam supply system with deep reinforcement learning," Energy, Elsevier, vol. 286(C).
    2. Dong, Zhe & Li, Junyi & Zhang, Jiasen & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2024. "Nonlinear finite-set control of clean energy systems with nuclear power application," Energy, Elsevier, vol. 313(C).
    3. Wang, Haotong & Li, Yanjun & Lin, Chaojing & Yang, Siyuan & Li, Guolong & Sun, Shengdi & Tian, Ye & Shi, Jianxin, 2024. "Research on condition assessment of nuclear power systems based on fault severity and fault harmfulness," Energy, Elsevier, vol. 311(C).
    4. Islam, Md. Monirul & Shahbaz, Muhammad & Samargandi, Nahla, 2024. "The nexus between Russian uranium exports and US nuclear-energy consumption: Do the spillover effects of geopolitical risks matter?," Energy, Elsevier, vol. 293(C).
    5. Li, Zheng & Guo, Chong & Wang, Linna & Zeng, Wenjie, 2024. "A multi-objective co-optimization method of controller parameters for the overall system of small pressurized water reactor," Energy, Elsevier, vol. 308(C).
    6. Hui, Jiuwu, 2024. "Discrete-time integral terminal sliding mode load following controller coupled with disturbance observer for a modular high-temperature gas-cooled reactor," Energy, Elsevier, vol. 292(C).
    7. Jiang, Qingfeng & Wang, Pengfei, 2025. "NSGA-II algorithm based control parameters optimization strategy for megawatt novel nuclear power systems," Energy, Elsevier, vol. 316(C).
    8. Hui, Jiuwu, 2024. "Coordinated discrete-time super-twisting sliding mode controller coupled with time-delay estimator for PWR-based nuclear steam supply system," Energy, Elsevier, vol. 301(C).
    9. Fan Li & Hongzhen Wang & Dong Liu & Ke Sun, 2025. "A Review of Multi-Temporal Scale Regulation Requirements of Power Systems and Diverse Flexible Resource Applications," Energies, MDPI, vol. 18(3), pages 1-32, January.
    10. Jie Chen & Kai Xiao & Ke Huang & Zhen Yang & Qing Chu & Guanfu Jiang, 2025. "A Multi-Variable Coupled Control Strategy Based on a Deep Deterministic Policy Gradient Reinforcement Learning Algorithm for a Small Pressurized Water Reactor," Energies, MDPI, vol. 18(6), pages 1-26, March.

    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:313:y:2024:i:c:s0360544224035527. 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.