Evaluating the reliability of machine-learning-based predictions used in nuclear power plant instrumentation and control systems

Chen, Edward; Bao, Han; Dinh, Nam

doi:10.1016/j.ress.2024.110266

Title: Evaluating the reliability of machine-learning-based predictions used in nuclear power plant instrumentation and control systems

Journal Article · 01 October 2024 · Reliability Engineering and System Safety

DOI: https://doi.org/10.1016/j.ress.2024.110266 · OSTI ID:2382850

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; Dinh, Nam

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Sponsoring Organization:: USDOE

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 2382850

Journal Information:: Reliability Engineering and System Safety, Journal Name: Reliability Engineering and System Safety Journal Issue: C Vol. 250; ISSN 0951-8320

Publisher:: ElsevierCopyright Statement

Country of Publication:: United Kingdom

Language:: English

References (22)

Modeling the bathtub shape hazard rate function in terms of reliability Wang, K. S.; Hsu, F. S.; Liu, P. P. Reliability Engineering & System Safety, Vol. 75, Issue 3 https://doi.org/10.1016/S0951-8320(01)00124-7	journal	March 2002
Development and assessment of a nearly autonomous management and control system for advanced reactors Lin, Linyu; Athe, Paridhi; Rouxelin, Pascal Annals of Nuclear Energy, Vol. 150 https://doi.org/10.1016/j.anucene.2020.107861	journal	January 2021
Uncertainty quantification and software risk analysis for digital twins in the nearly autonomous management and control systems: A review Lin, Linyu; Bao, Han; Dinh, Nam Annals of Nuclear Energy, Vol. 160 https://doi.org/10.1016/j.anucene.2021.108362	journal	September 2021
Integration of neural networks with numerical solution of PDEs for closure models development Iskhakov, Arsen S.; Dinh, Nam T.; Chen, Edward Physics Letters A, Vol. 406 https://doi.org/10.1016/j.physleta.2021.127456	journal	August 2021
Machine learning for reliability engineering and safety applications: Review of current status and future opportunities Xu, Zhaoyi; Saleh, Joseph Homer Reliability Engineering & System Safety, Vol. 211 https://doi.org/10.1016/j.ress.2021.107530	journal	July 2021
Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry Theissler, Andreas; Pérez-Velázquez, Judith; Kettelgerdes, Marcel Reliability Engineering & System Safety, Vol. 215 https://doi.org/10.1016/j.ress.2021.107864	journal	November 2021
Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges Xu, Yanwen; Kohtz, Sara; Boakye, Jessica Reliability Engineering & System Safety, Vol. 230 https://doi.org/10.1016/j.ress.2022.108900	journal	February 2023
Quantitative evaluation of common cause failures in high safety-significant safety-related digital instrumentation and control systems in nuclear power plants Bao, Han; Zhang, Hongbin; Shorthill, Tate Reliability Engineering & System Safety, Vol. 230 https://doi.org/10.1016/j.ress.2022.108973	journal	February 2023
Machine learning and model driven bayesian uncertainty quantification in suspended nonstructural systems Qin, Zhiyuan; Naser, M. Z. Reliability Engineering & System Safety, Vol. 237 https://doi.org/10.1016/j.ress.2023.109392	journal	September 2023
A novel digital twin-driven approach based on physical-virtual data fusion for gearbox fault diagnosis Xia, Jingyan; Huang, Ruyi; Chen, Zhuyun Reliability Engineering & System Safety, Vol. 240 https://doi.org/10.1016/j.ress.2023.109542	journal	December 2023
Data-physics-model based fatigue reliability assessment methodology for high-temperature components and its application in steam turbine rotor Gu, Hang-Hang; Wang, Run-Zi; Tang, Min-Jin Reliability Engineering & System Safety, Vol. 241 https://doi.org/10.1016/j.ress.2023.109633	journal	January 2024
Health indicators for remaining useful life prediction of complex systems based on long short-term memory network and improved particle filter Zhang, Yadong; Zhang, Chao; Wang, Shaoping Reliability Engineering & System Safety, Vol. 241 https://doi.org/10.1016/j.ress.2023.109666	journal	January 2024
A data-driven methodology with a nonparametric reliability method for optimal condition-based maintenance strategies Mikhail, Mina; Ouali, Mohamed-Salah; Yacout, Soumaya Reliability Engineering & System Safety, Vol. 241 https://doi.org/10.1016/j.ress.2023.109668	journal	January 2024
Physics-guided recurrent neural network trained with approximate Bayesian computation: A case study on structural response prognostics Fernández, Juan; Chiachío, Juan; Barros, José Reliability Engineering & System Safety, Vol. 243 https://doi.org/10.1016/j.ress.2023.109822	journal	March 2024
Robust deep Gaussian process-based trustworthy fog-haze-caused pollution flashover prediction approach for overhead contact lines Wang, Jian; Liu, Huiyuan; Gao, Shibin Reliability Engineering & System Safety, Vol. 243 https://doi.org/10.1016/j.ress.2023.109829	journal	March 2024
A principled distance-aware uncertainty quantification approach for enhancing the reliability of physics-informed neural network Li, Jinwu; Long, Xiangyun; Deng, Xinyang Reliability Engineering & System Safety, Vol. 245 https://doi.org/10.1016/j.ress.2024.109963	journal	May 2024
Trustworthiness modeling and evaluation for a nearly autonomous management and control system Wang, Longcong; Lin, Linyu; Dinh, Nam Reliability Engineering & System Safety, Vol. 245 https://doi.org/10.1016/j.ress.2024.110008	journal	May 2024
Benchmark of GOTHIC to EBR-II SHRT-17 and SHRT-45R Tests Lane, J. W.; Link, J. M.; King, J. M. Nuclear Technology, Vol. 206, Issue 7 https://doi.org/10.1080/00295450.2019.1698896	journal	January 2020
Comparison of Data-Driven Bandwidth Selectors Park, Byeong U.; Marron, J. S. Journal of the American Statistical Association, Vol. 85, Issue 409 https://doi.org/10.1080/01621459.1990.10475307	journal	March 1990
On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density Functions IEEE Transactions on Computers, Vol. C-25, Issue 11 https://doi.org/10.1109/TC.1976.1674577	journal	November 1976
A Reliable Data-Based Bandwidth Selection Method for Kernel Density Estimation Sheather, S. J.; Jones, M. C. Journal of the Royal Statistical Society: Series B (Methodological), Vol. 53, Issue 3 https://doi.org/10.1111/j.2517-6161.1991.tb01857.x	journal	July 1991
An overview of advances in reliability estimation of individual predictions in machine learning Bosnić, Zoran; Kononenko, Igor Intelligent Data Analysis, Vol. 13, Issue 2 https://doi.org/10.3233/IDA-2009-0371	journal	April 2009

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