IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v236y2022i4p542-553.html
   My bibliography  Save this article

Condition-based maintenance for the offshore wind turbine based on long short-term memory network

Author

Listed:
  • Yu Sun
  • Jichuan Kang
  • Liping Sun
  • Peng Jin
  • Xu Bai

Abstract

This paper introduces a condition-based maintenance method combined with long short-term memory network for offshore wind turbine. According to the ranking of offshore wind turbine components using multiple indicators (failure rate, repair time, and maintenance cost), the optimization object focuses on four critical components, namely, rotor, pitch system, gearbox, and generator. Long short-term memory network is implemented to evaluate system condition and predict potential risks, then the preventive maintenance can be performed on the component that reaches the reliability threshold. The repair activity provides an advance maintenance opportunity for the other components, sharing the fix maintenance costs and the downtime. A maintenance decision process is presented in this paper, aiming to achieve the maximum cost savings. Calculated and comparative results demonstrate that the policy proposed in this article is superior in validity and accuracy.

Suggested Citation

  • Yu Sun & Jichuan Kang & Liping Sun & Peng Jin & Xu Bai, 2022. "Condition-based maintenance for the offshore wind turbine based on long short-term memory network," Journal of Risk and Reliability, , vol. 236(4), pages 542-553, August.
    • Handle: RePEc:sae:risrel:v:236:y:2022:i:4:p:542-553
    DOI: 10.1177/1748006X20965434
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X20965434
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X20965434?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
    ---><---

    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:sae:risrel:v:236:y:2022:i:4:p:542-553. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: SAGE Publications (email available below). General contact details of provider: .

    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.