IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i11p1817-d1667426.html
   My bibliography  Save this article

A Sequential-Interval Optimal Sampling Strategy Based on Reliability Prediction Under Wiener Process

Author

Listed:
  • Mengying Ren

    (School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China)

  • Yubin Tian

    (Faculty of Computational Mathematics and Cybernetics, Shenzhen MSU-BIT University, Shenzhen 518172, China)

  • Xingyu Liu

    (School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China)

  • Furi Guo

    (Department of Mathematics and Statistics, Shanxi Datong University, Datong 037009, China)

Abstract

For satellite electronic components characterized by high reliability and long lifespan, achieving improved efficiency in reliability prediction is essential when only a limited amount of data is available. Many studies have collected degradation data using uniform sampling strategies. In this work, we propose sequential-interval G- and D-optimal sampling strategies for in-orbit degradation data collection based on the Wiener process, aiming to enhance the efficiency of reliability prediction. Finally, a simulation study is performed to verify the effectiveness of the proposed strategies. This study utilizes both linear and nonlinear models of satellite MOSFETs and employs the Monte Carlo method.

Suggested Citation

  • Mengying Ren & Yubin Tian & Xingyu Liu & Furi Guo, 2025. "A Sequential-Interval Optimal Sampling Strategy Based on Reliability Prediction Under Wiener Process," Mathematics, MDPI, vol. 13(11), pages 1-13, May.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:11:p:1817-:d:1667426
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/11/1817/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/11/1817/
    Download Restriction: no
    ---><---

    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:gam:jmathe:v:13:y:2025:i:11:p:1817-:d:1667426. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.