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Reliability modeling and a statistical inference method of accelerated degradation testing with multiple stresses and dependent competing failure processes

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  • Liu, Yao
  • Wang, Yashun
  • Fan, Zhengwei
  • Bai, Guanghan
  • Chen, Xun

Abstract

In this paper, a multiple stresses reliability model with dependent competing failure processes (DCFPs) is proposed, which includes constructing the multiple stresses acceleration model and deriving the degradation-shock dependences competing model. The multiple stresses coupling are considered in multiple stresses acceleration model, and the degradation-shock dependence is considered and modeled by Facilitation model. Then, a statistical inference method of accelerated degradation testing with multiple stresses and dependent competing failure processes is proposed. Finally, a practical example is used to demonstrate accuracy of the proposed model and method. It is shown that the reliability model without considering multiple environment stresses is a special case (40°C, 65%RH and random shocks) of that considering multiple environment stresses. We also explain the phenomenon that the reliability at same time is lower with the larger temperature, larger humidity and more random shock due to that the mean of wear rate is larger with the larger stress, and the reliability is lower with the larger mean of wear rate. Moreover, the maximum MSE of the parameter estimation result obtained by the statistical inference method is 0.34%.

Suggested Citation

  • Liu, Yao & Wang, Yashun & Fan, Zhengwei & Bai, Guanghan & Chen, Xun, 2021. "Reliability modeling and a statistical inference method of accelerated degradation testing with multiple stresses and dependent competing failure processes," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:reensy:v:213:y:2021:i:c:s0951832021001897
    DOI: 10.1016/j.ress.2021.107648
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    Cited by:

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    3. Chen, Wen-Bin & Li, Xiao-Yang & Kang, Rui, 2022. "Integration for degradation analysis with multi-source ADT datasets considering dataset discrepancies and epistemic uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    4. 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).
    5. Huang, Yeu-Shiang & Fang, Chih-Chiang & Lu, Chang-Ming & (Bill) Tseng, Tzu-Liang, 2022. "Optimal Warranty Policy for Consumer Electronics with Dependent Competing Failure Processes," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. Wu, Bei & Ding, Dong, 2022. "A gamma process based model for systems subject to multiple dependent competing failure processes under Markovian environments," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    7. Lyu, Hao & Qu, Hongchen & Yang, Zaiyou & Ma, Li & Lu, Bing & Pecht, Michael, 2023. "Reliability analysis of dependent competing failure processes with time-varying δ shock model," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    8. Wu, Bei & Wei, Xiaohua & Zhang, Yamei & Bai, Sijun, 2023. "Modeling dynamic environment effects on dependent failure processes with varying failure thresholds," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    9. 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).
    10. Chen, Wen-Bin & Li, Xiao-Yang & Wu, Ji-Peng & Kang, Rui, 2024. "Uncertain random accelerated degradation modelling and statistical analysis with aleatory and epistemic uncertainties from multiple dimensions," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
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