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Robust on-line diagnosis tool for the early accident detection in nuclear power plants

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  • Tolo, Silvia
  • Tian, Xiange
  • Bausch, Nils
  • Becerra, Victor
  • Santhosh, T.V.
  • Vinod, G.
  • Patelli, Edoardo

Abstract

Any loss of coolant accident mitigation strategy is necessarily bound by the promptness of the break detection as well as the accuracy of its diagnosis. The availability of on-line monitoring tools is then crucial for enhancing safety of nuclear facilities. The requirements of robustness and short latency implied by the necessity for fast and effective actions are undermined by the challenges associated with break prediction during transients.

Suggested Citation

  • Tolo, Silvia & Tian, Xiange & Bausch, Nils & Becerra, Victor & Santhosh, T.V. & Vinod, G. & Patelli, Edoardo, 2019. "Robust on-line diagnosis tool for the early accident detection in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 110-119.
    • Handle: RePEc:eee:reensy:v:186:y:2019:i:c:p:110-119
    DOI: 10.1016/j.ress.2019.02.015
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    References listed on IDEAS

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    1. Sanchez-Saez, F. & Sánchez, A.I. & Villanueva, J.F. & Carlos, S. & Martorell, S., 2018. "Uncertainty analysis of a large break loss of coolant accident in a pressurized water reactor using non-parametric methods," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 19-28.
    2. Santosh, T.V. & Srivastava, A. & Sanyasi Rao, V.V.S. & Ghosh, A.K. & Kushwaha, H.S., 2009. "Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 759-762.
    3. Magnus Nørgaard & Ole Ravn & Niels Kjølstad Poulsen, 2002. "NNSYSID-Toolbox for System Identification with Neural Networks," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 8(1), pages 1-20, March.
    4. Nabeshima, K & Suzudo, T & Ohno, T & Kudo, K, 2002. "Nuclear reactor monitoring with the combination of neural network and expert system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 60(3), pages 233-244.
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    Cited by:

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    3. Vincenzo Destino & Nicola Pedroni & Roberto Bonifetto & Francesco Di Maio & Laura Savoldi & Enrico Zio, 2021. "Metamodeling and On-Line Clustering for Loss-of-Flow Accident Precursors Identification in a Superconducting Magnet Cryogenic Cooling Circuit," Energies, MDPI, vol. 14(17), pages 1-37, September.
    4. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
    5. Li, Ruopu & Arzaghi, Ehsan & Abbassi, Rouzbeh & Chen, Diyi & Li, Chunhao & Li, Huanhuan & Xu, Beibei, 2020. "Dynamic maintenance planning of a hydro-turbine in operational life cycle," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    6. Wang, Fu & Xiahou, Tangfan & Zhang, Xian & He, Pan & Yang, Taibo & Niu, Jiang & Liu, Caixue & Liu, Yu, 2024. "Convolutional preprocessing Transformer-based fault diagnosis for rectifier-filter circuits in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    7. Liu, Jiaxin & Yu, Deping & Yang, Taibo & Liu, Caixue & Wang, Guangjin & Liu, Xiaoming, 2023. "Discovering the causes for the change of the vibration characteristics of the core support barrel in PWR nuclear power plants: A combined investigation based on ex-core neutron noise analysis and nume," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    8. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    9. Wu, Jingyao & Zhao, Zhibin & Sun, Chuang & Yan, Ruqiang & Chen, Xuefeng, 2021. "Learning from Class-imbalanced Data with a Model-Agnostic Framework for Machine Intelligent Diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).

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