IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v238y2023ics0951832023003198.html
   My bibliography  Save this article

Remaining useful life prediction via a hybrid DBN-KF-based method: A case of subsea Christmas tree valves

Author

Listed:
  • Shao, Xiaoyan
  • Cai, Baoping
  • Liu, Yonghong
  • Zhang, Junyan
  • Sui, Zhongfei
  • Feng, Qiang

Abstract

Remaining useful life (RUL) is the critical goal of fault prediction, which provides theoretical support for subsequent maintenance decisions of a system. The state measurement of industrial equipment is often accompanied by a large amount of random noise. In addition, the parameters of the degradation are often random. This kind of uncertainty makes carrying out RUL prediction difficult. To this end, a novel hybrid model-data-driven RUL prediction method based on a fusion of Kalman filter (KF) and dynamic Bayesian network (DBN) is proposed in this paper. The hybrid DBN-KF-based method provides a more comprehensive evaluation and improves accuracy compared with the traditional KF method. By enhancing the performance of observation values through DBN, the optimal estimation of the system state is implemented. Estimation error and observation error are fully considered. In addition, the uncertainty distribution of degradation parameters and environmental parameters is integrated into the state estimation model. RUL is determined based on the system state calculated by the proposed method and the failure threshold obtained by the system characteristics. Numerical simulation is carried out for a subsea Christmas tree valves to demonstrate the advantages of the proposed RUL prediction method.

Suggested Citation

  • Shao, Xiaoyan & Cai, Baoping & Liu, Yonghong & Zhang, Junyan & Sui, Zhongfei & Feng, Qiang, 2023. "Remaining useful life prediction via a hybrid DBN-KF-based method: A case of subsea Christmas tree valves," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
  • Handle: RePEc:eee:reensy:v:238:y:2023:i:c:s0951832023003198
    DOI: 10.1016/j.ress.2023.109405
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023003198
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2023.109405?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tao, Haohan & Jia, Peng & Wang, Xiangyu & Wang, Liquan, 2024. "Reliability analysis of subsea control module based on dynamic Bayesian network and digital twin," Reliability Engineering and System Safety, Elsevier, vol. 248(C).

    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:238:y:2023:i:c:s0951832023003198. 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: 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.