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Planning of step-stress accelerated degradation test based on the inverse Gaussian process

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  • Wang, Huan
  • Wang, Guan-jun
  • Duan, Feng-jun

Abstract

The step-stress accelerated degradation test (SSADT) is a useful tool for assessing the lifetime distribution of highly reliable or expensive product. Some efficient SSADT plans have been proposed when the underlying degradation follows the Wiener process or Gamma process. However, how to design an efficient SSADT plan for the inverse Gaussian (IG) process is still a problem to be solved. The aim of this paper is to provide an optimal SSADT plan for the IG degradation process. A cumulative exposure model for the SSADT is adopted, in which the product degradation path depends only on the current stress level and the degradation accumulated, and has nothing to do with the way of accumulation. Under the constraint of the total experimental budget, some design variables are optimized by minimizing the asymptotic variance of the estimated p-quantile of the lifetime distribution of the product. Finally, we use the proposed method to deal with the optimal SSADT design for a type of electrical connector based on a set of stress relaxation data. The sensitivity and stability of the SSADT plan are studied, and we find that the optimal test plan is quite robust for a moderate departure from the values of the parameters.

Suggested Citation

  • Wang, Huan & Wang, Guan-jun & Duan, Feng-jun, 2016. "Planning of step-stress accelerated degradation test based on the inverse Gaussian process," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 97-105.
    • Handle: RePEc:eee:reensy:v:154:y:2016:i:c:p:97-105
    DOI: 10.1016/j.ress.2016.05.018
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    References listed on IDEAS

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    1. Wang, Lizhi & Pan, Rong & Li, Xiaoyang & Jiang, Tongmin, 2013. "A Bayesian reliability evaluation method with integrated accelerated degradation testing and field information," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 38-47.
    2. Naijun Sha & Rong Pan, 2014. "Bayesian analysis for step-stress accelerated life testing using weibull proportional hazard model," Statistical Papers, Springer, vol. 55(3), pages 715-726, August.
    3. Peng, Weiwen & Li, Yan-Feng & Yang, Yuan-Jian & Huang, Hong-Zhong & Zuo, Ming J., 2014. "Inverse Gaussian process models for degradation analysis: A Bayesian perspective," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 175-189.
    4. Wang, Xiao, 2010. "Wiener processes with random effects for degradation data," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 340-351, February.
    5. Zhang, Chunhua & Lu, Xiang & Tan, Yuanyuan & Wang, Yashun, 2015. "Reliability demonstration methodology for products with Gamma Process by optimal accelerated degradation testing," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 369-377.
    6. Bae, Suk Joo & Kuo, Way & Kvam, Paul H., 2007. "Degradation models and implied lifetime distributions," Reliability Engineering and System Safety, Elsevier, vol. 92(5), pages 601-608.
    7. Goh, Gyuhyeong & Dey, Dipak K., 2014. "Bayesian model diagnostics using functional Bregman divergence," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 371-383.
    8. Pan, Zhengqiang & Balakrishnan, Narayanaswamy, 2011. "Reliability modeling of degradation of products with multiple performance characteristics based on gamma processes," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 949-957.
    9. Ye, Zhi-Sheng & Chen, Nan & Shen, Yan, 2015. "A new class of Wiener process models for degradation analysis," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 58-67.
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    Cited by:

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    3. Yan, Weian & Xu, Xiaofan & Bigaud, David & Cao, Wenqin, 2023. "Optimal design of step-stress accelerated degradation tests based on the Tweedie exponential dispersion process," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Zheng, Bokai & Chen, Cen & Lin, Yigang & Hu, Yifan & Ye, Xuerong & Zhai, Guofu & Zio, Enrico, 2022. "Optimal design of step-stress accelerated degradation test oriented by nonlinear and distributed degradation process," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    5. Lu, Yaohui & Zheng, Heyan & Zeng, Jing & Chen, Tianli & Wu, Pingbo, 2019. "Fatigue life reliability evaluation in a high-speed train bogie frame using accelerated life and numerical test," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 221-232.
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    7. Cheng, Yao & Liao, Haitao & Huang, Zhiyi, 2021. "Optimal degradation-based hybrid double-stage acceptance sampling plan for a heterogeneous product," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    8. Hao, Songhua & Yang, Jun & Berenguer, Christophe, 2018. "Nonlinear step-stress accelerated degradation modelling considering three sources of variability," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 207-215.
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    10. Zhang, Zhengxin & Si, Xiaosheng & Hu, Changhua & Lei, Yaguo, 2018. "Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods," European Journal of Operational Research, Elsevier, vol. 271(3), pages 775-796.

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