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An approach for reliability prediction of instrumentation & control cables by artificial neural networks and Weibull theory for probabilistic safety assessment of NPPs

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  • Santhosh, T.V.
  • Gopika, V.
  • Ghosh, A.K.
  • Fernandes, B.G.

Abstract

The polymeric materials used for insulation and sheath in instrumentation and control (I&C) cables of nuclear power plants (NPPs) are subjected to degradation due to various stressors. The prediction of long-term aging and lifetime of cables is generally determined based on accelerated life testing (ALT) experiments which are not only expensive but also time consuming. Application of artificial neural networks (ANNs) in the field of transient diagnosis and condition assessment of electrical and other equipment has been a promising technique; however the use of ANN for reliability prediction of I&C cables has not yet been studied. This paper presents an integrated approach to predict the lifetime and reliability of I&C cables by ANN from the accelerated aging data. In order to validate the proposed methodology for use in probabilistic safety assessment (PSA) of NPP to account for the cable failures, ALT data on a typical cross-linked polyethylene (XLPE) insulated I&C cable has been referred from the literature. The time dependent reliability was predicted by considering the various failure rates. Study demonstrates that by an appropriate training algorithm with suitable network architecture, it is possible to predict the reliability of I&C cables by ANN with the minimal accelerated life testing.

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  • Santhosh, T.V. & Gopika, V. & Ghosh, A.K. & Fernandes, B.G., 2018. "An approach for reliability prediction of instrumentation & control cables by artificial neural networks and Weibull theory for probabilistic safety assessment of NPPs," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 31-44.
    • Handle: RePEc:eee:reensy:v:170:y:2018:i:c:p:31-44
    DOI: 10.1016/j.ress.2017.10.010
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    Cited by:

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    2. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Min Ho Kim & Hyun Jeong Seo & Sang Kyu Lee & Min Chul Lee, 2021. "Influence of Thermal Aging on the Combustion Characteristics of Cables in Nuclear Power Plants," Energies, MDPI, vol. 14(7), pages 1-17, April.
    4. Dong, Zhe & Li, Bowen & Li, Junyi & Huang, Xiaojin & Zhang, Zuoyi, 2022. "Online reliability assessment of energy systems based on a high-order extended-state-observer with application to nuclear reactors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    5. Koutsellis, Themistoklis & Nikas, Alexandros, 2020. "A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

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