IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v95y2010i1p49-57.html
   My bibliography  Save this article

A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system

Author

Listed:
  • Zio, Enrico
  • Di Maio, Francesco

Abstract

This paper presents a similarity-based approach for prognostics of the Remaining Useful Life (RUL) of a system, i.e. the lifetime remaining between the present and the instance when the system can no longer perform its function. Data from failure dynamic scenarios of the system are used to create a library of reference trajectory patterns to failure. Given a failure scenario developing in the system, the remaining time before failure is predicted by comparing by fuzzy similarity analysis its evolution data to the reference trajectory patterns and aggregating their times to failure in a weighted sum which accounts for their similarity to the developing pattern. The prediction on the failure time is dynamically updated as time goes by and measurements of signals representative of the system state are collected. The approach allows for the on-line estimation of the RUL. For illustration, a case study is considered regarding the estimation of RUL in failure scenarios of the Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS).

Suggested Citation

  • Zio, Enrico & Di Maio, Francesco, 2010. "A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 49-57.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:1:p:49-57
    DOI: 10.1016/j.ress.2009.08.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832009002051
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2009.08.001?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. Zimmermann, H. -J. & Zysno, P., 1985. "Quantifying vagueness in decision models," European Journal of Operational Research, Elsevier, vol. 22(2), pages 148-158, November.
    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.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Medaglia, Andres L. & Fang, Shu-Cherng & Nuttle, Henry L. W. & Wilson, James R., 2002. "An efficient and flexible mechanism for constructing membership functions," European Journal of Operational Research, Elsevier, vol. 139(1), pages 84-95, May.
    3. Tzeng, Gwo-Hshiung & Teodorovic, Dusan & Hwang, Ming-Jiu, 1996. "Fuzzy bicriteria multi-index transportation problems for coal allocation planning of Taipower," European Journal of Operational Research, Elsevier, vol. 95(1), pages 62-72, November.
    4. 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).
    5. Gómez, M.J. & Castejón, C. & García-Prada, J.C., 2016. "Automatic condition monitoring system for crack detection in rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 239-247.
    6. 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.
    7. Oh, ChoHwan & Lee, Jeong Ik, 2020. "Real time nuclear power plant operating state cognitive algorithm development using dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    8. Peng Su & Guanjun Wang, 2022. "Reliability analysis of network systems subject to probabilistic propagation failures and failure isolation effects," Journal of Risk and Reliability, , vol. 236(2), pages 290-306, April.

    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:95:y:2010:i:1:p:49-57. 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.