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Improved inverse Gaussian process and bootstrap: Degradation and reliability metrics

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  • Guo, Jingbo
  • Wang, Changxi
  • Cabrera, Javier
  • Elsayed, Elsayed A.

Abstract

The inverse Gaussian (IG) process is commonly used in modeling monotonically increasing degradation processes. Traditional degradation modeling considers the process parameters as functions of time and environmental conditions. However, in many practical situations, the degradation increment in the next time interval may depend on degradation state at the current time interval. Therefore, in this paper, we propose an improved inverse Gaussian (IIG) process which considers the dependency between degradation increments and prior degradation states. Reliability metrics of the IIG process are estimated and validated using crack length growth data as well as simulated degradation data. Results show that the proposed model provides more accurate reliability metrics than the IG process model. Bootstrap of degradation increments or detrended degradation increments is introduced as a supplementary method to estimate the remaining life probability interval.

Suggested Citation

  • Guo, Jingbo & Wang, Changxi & Cabrera, Javier & Elsayed, Elsayed A., 2018. "Improved inverse Gaussian process and bootstrap: Degradation and reliability metrics," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 269-277.
    • Handle: RePEc:eee:reensy:v:178:y:2018:i:c:p:269-277
    DOI: 10.1016/j.ress.2018.06.013
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    References listed on IDEAS

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    Cited by:

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    3. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Hao, Songhua & Yang, Jun & Berenguer, Christophe, 2019. "Degradation analysis based on an extended inverse Gaussian process model with skew-normal random effects and measurement errors," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 261-270.
    5. Mikhail, Mina & Ouali, Mohamed-Salah & Yacout, Soumaya, 2024. "A data-driven methodology with a nonparametric reliability method for optimal condition-based maintenance strategies," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Salem, Marwa Belhaj & Fouladirad, Mitra & Deloux, Estelle, 2022. "Variance Gamma process as degradation model for prognosis and imperfect maintenance of centrifugal pumps," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    7. Li, Zan & Wang, Fengming & Wang, Chengjie & Hu, Qingpei & Yu, Dan, 2021. "Reliability modeling and evaluation of lifetime delayed degradation process with nondestructive testing," Reliability Engineering and System Safety, Elsevier, vol. 208(C).

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