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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
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    References listed on IDEAS

    as
    1. Wang, Xiaolin & Balakrishnan, Narayanaswamy & Guo, Bo, 2014. "Residual life estimation based on a generalized Wiener degradation process," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 13-23.
    2. Zhang, Shuyi & Zhai, Qingqing & Li, Yaqiu, 2023. "Degradation modeling and RUL prediction with Wiener process considering measurable and unobservable external impacts," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
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