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Optimal design of hybrid accelerated test based on the Inverse Gaussian process model

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  • Ma, Zhonghai
  • Liao, Haitao
  • Ji, Hui
  • Wang, Shaoping
  • Yin, Fanglong
  • Nie, Songlin

Abstract

Accelerated life testing (ALT) and accelerated degradation testing (ADT) are two widely used accelerated testing (AT) methods for reliability evaluation of highly reliable products. However, in many cases, it is difficult to meet the estimation requirements only using ALT or ADT because of the nature of data and the test constraints. Since both ALT and ADT data provide useful reliability information, it would be quite valuable to use both of them for reliability estimation. In this paper, an optimal testing plan is proposed for the first time for such a hybrid AT under several experimental design constraints. An Inverse Gaussian (IG) process and the corresponding lifetime distribution are used to model the degradation process and the lifetime of the product, respectively. The strategy of allocating the test units to ALT and ADT and determining the data collection strategy in the experiment are presented. The objective is to maximize the estimation precision of the p-quantile of the product's lifetime under the normal use condition. A study on the stress relaxation of a type of electrical connector is conducted to illustrate the value of the proposed method in practice.

Suggested Citation

  • Ma, Zhonghai & Liao, Haitao & Ji, Hui & Wang, Shaoping & Yin, Fanglong & Nie, Songlin, 2021. "Optimal design of hybrid accelerated test based on the Inverse Gaussian process model," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:reensy:v:210:y:2021:i:c:s0951832021000715
    DOI: 10.1016/j.ress.2021.107509
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    References listed on IDEAS

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    1. 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.
    2. Wang, Han & Zhao, Yu & Ma, Xiaobing & Wang, Hongyu, 2017. "Optimal design of constant-stress accelerated degradation tests using the M-optimality criterion," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 45-54.
    3. Soumya Roy & Chiranjit Mukhopadhyay, 2016. "Bayesian D -optimal Accelerated Life Test plans for series systems with competing exponential causes of failure," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(8), pages 1477-1493, June.
    4. 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.
    5. Liu, Di & Wang, Shaoping & Zhang, Chao & Tomovic, Mileta, 2018. "Bayesian model averaging based reliability analysis method for monotonic degradation dataset based on inverse Gaussian process and Gamma process," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 25-38.
    6. Ma, Zhonghai & Wang, Shaoping & Ruiz, Cesar & Zhang, Chao & Liao, Haitao & Pohl, Edward, 2020. "Reliability estimation from two types of accelerated testing data considering measurement error," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
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    Cited by:

    1. Li, Naipeng & Wang, Mingyang & Lei, Yaguo & Si, Xiaosheng & Yang, Bin & Li, Xiang, 2024. "A nonparametric degradation modeling method for remaining useful life prediction with fragment data," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    2. Liu, Zhe & Li, Xiaoyang & Kang, Rui, 2022. "Uncertain differential equation based accelerated degradation modeling," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    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. Li, Yang & Gao, Haifeng & Chen, Hongtian & Liu, Chun & Yang, Zhe & Zio, Enrico, 2024. "Accelerated degradation testing for lifetime analysis considering random effects and the influence of stress and measurement errors," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    6. Liu, Xingheng & Matias, José & Jäschke, Johannes & Vatn, Jørn, 2022. "Gibbs sampler for noisy Transformed Gamma process: Inference and remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    7. Ye, Xuerong & Hu, Yifan & Zheng, Bokai & Chen, Cen & Zhai, Guofu, 2022. "A new class of multi-stress acceleration models with interaction effects and its extension to accelerated degradation modelling," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    8. Chen, Xingyu & Yang, Qingyu & Wu, Xin, 2022. "Nonlinear degradation model and reliability analysis by integrating image covariate," Reliability Engineering and System Safety, Elsevier, vol. 225(C).

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