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Modified Information Criterion for Testing Changes in the Inverse Gaussian Degradation Process

Author

Listed:
  • Jiahua Qiao

    (School of Science, Hebei University of Science and Technology, Shijiazhuang 050018, China)

  • Xia Cai

    (School of Science, Hebei University of Science and Technology, Shijiazhuang 050018, China)

  • Meiqi Zhang

    (School of Science, Hebei University of Science and Technology, Shijiazhuang 050018, China)

Abstract

The Inverse Gaussian process is a useful stochastic process to model the monotonous degradation process of a certain component. Owing to the phenomenon that the degradation processes often exhibit multi-stage characteristics because of the internal degradation mechanisms and external environmental factors, a change-point Inverse Gaussian process is studied in this paper. A modified information criterion method is applied to illustrate the existence and estimate of the change point. A reliability function is derived based on the proposed method. The simulations are conducted to show the performance of the proposed method. As a result, the procedure outperforms the existing procedure with regard to test power and consistency. Finally, the procedure is applied to hydraulic piston pump data to demonstrate its practical application.

Suggested Citation

  • Jiahua Qiao & Xia Cai & Meiqi Zhang, 2025. "Modified Information Criterion for Testing Changes in the Inverse Gaussian Degradation Process," Mathematics, MDPI, vol. 13(4), pages 1-16, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:4:p:663-:d:1593491
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    References listed on IDEAS

    as
    1. Yuan, X.-X. & Pandey, M.D., 2009. "A nonlinear mixed-effects model for degradation data obtained from in-service inspections," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 509-519.
    2. Wang, Han & Liao, Haitao & Ma, Xiaobing, 2022. "Stochastic Multi-phase Modeling and Health Assessment for Systems Based on Degradation Branching Processes," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Bae, Suk Joo & Yuan, Tao & Ning, Shuluo & Kuo, Way, 2015. "A Bayesian approach to modeling two-phase degradation using change-point regression," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 66-74.
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