IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p1850-d1067342.html
   My bibliography  Save this article

Fault Diagnosis Techniques for Nuclear Power Plants: A Review from the Artificial Intelligence Perspective

Author

Listed:
  • Ben Qi

    (Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China)

  • Jingang Liang

    (Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China)

  • Jiejuan Tong

    (Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China)

Abstract

Fault diagnosis plays an important role in complex and safety-critical systems such as nuclear power plants (NPPs). With the development of artificial intelligence (AI), extensive research has been carried out for fast and efficient fault diagnosis based on intelligent methods. This paper presents a review of various AI-based system-level fault diagnosis methods for NPPs. We first discuss the development history of AI. Based on this exposition, AI-based fault diagnosis techniques are classified into knowledge-driven and data-driven approaches. For knowledge-driven methods, we discuss both the early if–then-based fault diagnosis techniques and the current new theory-based ones. The principles, application, and comparative analysis of the representative methods are systematically described. For data-driven strategies, we discuss single-algorithm-based techniques such as ANN, SVM, PCA, DT, and clustering, as well as hybrid techniques that combine algorithms together. The advantages and disadvantages of both knowledge-driven and data-driven methods are compared, illustrating the tendency to combine the two approaches. Finally, we provide some possible future research directions and suggestions.

Suggested Citation

  • Ben Qi & Jingang Liang & Jiejuan Tong, 2023. "Fault Diagnosis Techniques for Nuclear Power Plants: A Review from the Artificial Intelligence Perspective," Energies, MDPI, vol. 16(4), pages 1-27, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1850-:d:1067342
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/1850/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/4/1850/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Jiangkuan & Lin, Meng & Li, Yankai & Wang, Xu, 2022. "Transfer learning network for nuclear power plant fault diagnosis with unlabeled data under varying operating conditions," Energy, Elsevier, vol. 254(PB).
    2. Xinwei Cong & Caiping Zhang & Jiuchun Jiang & Weige Zhang & Yan Jiang & Linjing Zhang, 2021. "A Comprehensive Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 14(5), pages 1-21, February.
    3. Roberto Pierdicca & Marina Paolanti & Andrea Felicetti & Fabio Piccinini & Primo Zingaretti, 2020. "Automatic Faults Detection of Photovoltaic Farms: solAIr, a Deep Learning-Based System for Thermal Images," Energies, MDPI, vol. 13(24), pages 1-17, December.
    4. Tyler J. VanderWeele & James M. Robins, 2010. "Signed directed acyclic graphs for causal inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 111-127, January.
    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. Xingyu Xiao & Ben Qi & Jingang Liang & Jiejuan Tong & Qing Deng & Peng Chen, 2023. "Enhancing LOCA Breach Size Diagnosis with Fundamental Deep Learning Models and Optimized Dataset Construction," Energies, MDPI, vol. 17(1), pages 1-20, December.
    2. Zhiqiang Peng & Jichong Lei & Zining Ni & Tao Yu & Jinsen Xie & Jun Hong & Hong Hu, 2024. "Research on Data-Driven Methods for Solving High-Dimensional Neutron Transport Equations," Energies, MDPI, vol. 17(16), pages 1-11, August.
    3. Xingyu Xiao & Jingang Liang & Jiejuan Tong & Haitao Wang, 2024. "Emergency Decision Support Techniques for Nuclear Power Plants: Current State, Challenges, and Future Trends," Energies, MDPI, vol. 17(10), pages 1-35, May.
    4. Chenyang Lai & Ibrahim Ahmed & Enrico Zio & Wei Li & Yiwang Zhang & Wenqing Yao & Juan Chen, 2024. "A Multistage Physics-Informed Neural Network for Fault Detection in Regulating Valves of Nuclear Power Plants," Energies, MDPI, vol. 17(11), pages 1-23, May.

    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. Wenwei Wang & Shuaibang Liu & Xiao-Ying Ma & Jiuchun Jiang & Xiao-Guang Yang, 2024. "Advancing Smart Lithium-Ion Batteries: A Review on Multi-Physical Sensing Technologies for Lithium-Ion Batteries," Energies, MDPI, vol. 17(10), pages 1-15, May.
    2. Yang, Kuang & Liao, Haifan & Xu, Bo & Chen, Qiuxiang & Hou, Zhenghui & Wang, Haijun, 2024. "Data-driven dryout prediction in helical-coiled once-through steam generator: A physics-informed approach leveraging the Buckingham Pi theorem," Energy, Elsevier, vol. 294(C).
    3. Abdulla, Hind & Sleptchenko, Andrei & Nayfeh, Ammar, 2024. "Photovoltaic systems operation and maintenance: A review and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(C).
    4. Lijun Zhu & Jian Wang & Yutao Wang & Bin Pan & Lujun Wang, 2024. "Detection of Impedance Inhomogeneity in Lithium-Ion Battery Packs Based on Local Outlier Factor," Energies, MDPI, vol. 17(20), pages 1-20, October.
    5. Sanjay Chaudhuri, 2014. "Qualitative inequalities for squared partial correlations of a Gaussian random vector," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 345-367, April.
    6. Burhan Can Karahasan & Fırat Bilgel, 2020. "State–Business Relations, Financial Access and Firm Performance: A Causal Mediation Analysis," Journal of International Development, John Wiley & Sons, Ltd., vol. 32(7), pages 1033-1074, October.
    7. Mohamed Benghanem & Adel Mellit & Chourouk Moussaoui, 2023. "Embedded Hybrid Model (CNN–ML) for Fault Diagnosis of Photovoltaic Modules Using Thermographic Images," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    8. Gianfranco Di Lorenzo & Erika Stracqualursi & Leonardo Micheli & Salvatore Celozzi & Rodolfo Araneo, 2022. "Prognostic Methods for Photovoltaic Systems’ Underperformance and Degradation: Status, Perspectives, and Challenges," Energies, MDPI, vol. 15(17), pages 1-6, September.
    9. Juan-Pablo Villegas-Ceballos & Mateo Rico-Garcia & Carlos Andres Ramos-Paja, 2022. "Dataset for Detecting the Electrical Behavior of Photovoltaic Panels from RGB Images," Data, MDPI, vol. 7(6), pages 1-12, June.
    10. Jorge De La Cruz & Eduardo Gómez-Luna & Majid Ali & Juan C. Vasquez & Josep M. Guerrero, 2023. "Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends," Energies, MDPI, vol. 16(5), pages 1-37, February.
    11. 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).
    12. 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).
    13. 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).
    14. Jiang, Zhichao & Ding, Peng, 2017. "The directions of selection bias," Statistics & Probability Letters, Elsevier, vol. 125(C), pages 104-109.
    15. Rüther, Tom & Plank, Christian & Schamel, Maximilian & Danzer, Michael A., 2023. "Detection of inhomogeneities in serially connected lithium-ion batteries," Applied Energy, Elsevier, vol. 332(C).
    16. Tyler J. VanderWeele, 2013. "Surrogate Measures and Consistent Surrogates," Biometrics, The International Biometric Society, vol. 69(3), pages 561-565, September.
    17. Firat Bilgel, 2021. "Infant mortality in Turkey: Causes and effects in a regional context," Papers in Regional Science, Wiley Blackwell, vol. 100(2), pages 429-453, April.
    18. Bilal Taghezouit & Fouzi Harrou & Cherif Larbes & Ying Sun & Smail Semaoui & Amar Hadj Arab & Salim Bouchakour, 2022. "Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study," Energies, MDPI, vol. 15(21), pages 1-30, October.
    19. Mario Eduardo Carbonó dela Rosa & Graciela Velasco Herrera & Rocío Nava & Enrique Quiroga González & Rodolfo Sosa Echeverría & Pablo Sánchez Álvarez & Jaime Gandarilla Ibarra & Víctor Manuel Velasco H, 2023. "A New Methodology for Early Detection of Failures in Lithium-Ion Batteries," Energies, MDPI, vol. 16(3), pages 1-18, January.
    20. Di Tommaso, Antonio & Betti, Alessandro & Fontanelli, Giacomo & Michelozzi, Benedetto, 2022. "A multi-stage model based on YOLOv3 for defect detection in PV panels based on IR and visible imaging by unmanned aerial vehicle," Renewable Energy, Elsevier, vol. 193(C), pages 941-962.

    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:gam:jeners:v:16:y:2023:i:4:p:1850-:d:1067342. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.