IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v202y2020ics0951832020305330.html
   My bibliography  Save this article

Accident diagnosis algorithm with untrained accident identification during power-increasing operation

Author

Listed:
  • Yang, Jaemin
  • Kim, Jonghyun

Abstract

To ensure the safety of nuclear power plants (NPPs) from accidents or anomalies, regulatory bodies provide procedures that describe safety regulations that must be followed. However, even if well-designed procedures are provided to operators, diagnostic activity in an emergency scenario is classified as an extremely demanding task. Moreover, the diagnosis of accidents occurring under various operation modes, such as power increasing, is expected to be extremely difficult, owing to the diverse behaviors and availability of systems and components. With regard to such emergency response issues, artificial neural network-based methods are regarded as one of the most promising approaches, because of their noticeable achievements. However, regarding the application of neural networks, in the case of an untrained accident, there is no capability to answer “do not know.†This study aims to develop algorithms that can cover various NPP operation modes and deal with untrained accidents. To address the various NPP operation modes, the major changes that can affect the plant states are classified. Furthermore, to deal with untrained accidents, the applied diagnostic algorithms use long short-term memory and an autoencoder. Following this, this paper presents the implementation and test results of the accident diagnosis algorithms.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:202:y:2020:i:c:s0951832020305330
    DOI: 10.1016/j.ress.2020.107032
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832020305330
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2020.107032?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. Santosh, T.V. & Vinod, Gopika & Saraf, R.K. & Ghosh, A.K. & Kushwaha, H.S., 2007. "Application of artificial neural networks to nuclear power plant transient diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1468-1472.
    2. Salahshoor, Karim & Kordestani, Mojtaba & Khoshro, Majid S., 2010. "Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers," Energy, Elsevier, vol. 35(12), pages 5472-5482.
    3. Rocco S., Claudio M. & Zio, Enrico, 2007. "A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems," Reliability Engineering and System Safety, Elsevier, vol. 92(5), pages 593-600.
    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. Daeil Lee & Seoryong Koo & Inseok Jang & Jonghyun Kim, 2022. "Comparison of Deep Reinforcement Learning and PID Controllers for Automatic Cold Shutdown Operation," Energies, MDPI, vol. 15(8), pages 1-25, April.

    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. Guikema, Seth D., 2009. "Natural disaster risk analysis for critical infrastructure systems: An approach based on statistical learning theory," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 855-860.
    2. Quintanilha, Igor M. & Elias, Vitor R.M. & da Silva, Felipe B. & Fonini, Pedro A.M. & da Silva, Eduardo A.B. & Netto, Sergio L. & Apolinário, José A. & de Campos, Marcello L.R. & Martins, Wallace A., 2021. "A fault detector/classifier for closed-ring power generators using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    3. Ardvin Kester S. Ong & Jelline C. Cuales & Jose Pablo F. Custodio & Eisley Yuanne J. Gumasing & Paula Norlene A. Pascual & Ma. Janice J. Gumasing, 2023. "Investigating Preceding Determinants Affecting Primary School Students Online Learning Experience Utilizing Deep Learning Neural Network," Sustainability, MDPI, vol. 15(4), pages 1-24, February.
    4. Ota, Shuhei & Kimura, Mitsuhiro, 2017. "A statistical dependent failure detection method for n-component parallel systems," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 376-382.
    5. Krzysztof Gaska & Agnieszka Generowicz & Anna Gronba-Chyła & Józef Ciuła & Iwona Wiewiórska & Paweł Kwaśnicki & Marcin Mala & Krzysztof Chyła, 2023. "Artificial Intelligence Methods for Analysis and Optimization of CHP Cogeneration Units Based on Landfill Biogas as a Progress in Improving Energy Efficiency and Limiting Climate Change," Energies, MDPI, vol. 16(15), pages 1-19, July.
    6. Huang, Chung-Neng & Chen, Yui-Sung, 2017. "Design of magnetic flywheel control for performance improvement of fuel cells used in vehicles," Energy, Elsevier, vol. 118(C), pages 840-852.
    7. Feng Lu & Jipeng Jiang & Jinquan Huang & Xiaojie Qiu, 2018. "An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis," Energies, MDPI, vol. 11(7), pages 1-21, July.
    8. Chiwoo Park & Jianhua Z. Huang & Yu Ding, 2010. "A Computable Plug-In Estimator of Minimum Volume Sets for Novelty Detection," Operations Research, INFORMS, vol. 58(5), pages 1469-1480, October.
    9. Wen, Zhixun & Pei, Haiqing & Liu, Hai & Yue, Zhufeng, 2016. "A Sequential Kriging reliability analysis method with characteristics of adaptive sampling regions and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 170-179.
    10. Zhu, L. & Li, M.S. & Wu, Q.H. & Jiang, L., 2015. "Short-term natural gas demand prediction based on support vector regression with false neighbours filtered," Energy, Elsevier, vol. 80(C), pages 428-436.
    11. Yang, Chunzhen & Liu, Jingquan & Zeng, Yuyun & Xie, Guangyao, 2019. "Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model," Renewable Energy, Elsevier, vol. 133(C), pages 433-441.
    12. Li, Zhanhang & Zhou, Jian & Nassif, Hani & Coit, David & Bae, Jinwoo, 2023. "Fusing physics-inferred information from stochastic model with machine learning approaches for degradation prediction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    13. Zhang, Xinwei & Feng, Yong & Chen, Jinglong & Liu, Zijun & Wang, Jun & Huang, Hong, 2024. "Knowledge distillation-optimized two-stage anomaly detection for liquid rocket engine with missing multimodal data," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    14. Sun, Rongzhuo & Shi, Licheng & Yang, Xilian & Wang, Yuzhang & Zhao, Qunfei, 2020. "A coupling diagnosis method of sensors faults in gas turbine control system," Energy, Elsevier, vol. 205(C).
    15. Morshedizadeh, Majid & Kordestani, Mojtaba & Carriveau, Rupp & Ting, David S.-K. & Saif, Mehrdad, 2017. "Application of imputation techniques and Adaptive Neuro-Fuzzy Inference System to predict wind turbine power production," Energy, Elsevier, vol. 138(C), pages 394-404.
    16. Brkovic, Aleksandar & Gajic, Dragoljub & Gligorijevic, Jovan & Savic-Gajic, Ivana & Georgieva, Olga & Di Gennaro, Stefano, 2017. "Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery," Energy, Elsevier, vol. 136(C), pages 63-71.
    17. Martinez-Martinez, Sinuhe & Messai, Nadhir & Jeannot, Jean-Philippe & Nuzillard, Danielle, 2015. "Two neural network based strategies for the detection of a total instantaneous blockage of a sodium-cooled fast reactor," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 50-57.
    18. Hernandez-Perdomo, Elvis & Guney, Yilmaz & Rocco, Claudio M., 2019. "A reliability model for assessing corporate governance using machine learning techniques," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 220-231.
    19. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    20. Zhou, Dengji & Zhang, Huisheng & Weng, Shilie, 2014. "A novel prognostic model of performance degradation trend for power machinery maintenance," Energy, Elsevier, vol. 78(C), pages 740-746.

    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:reensy:v:202:y:2020:i:c:s0951832020305330. 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: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.