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Design of PH-based accelerated life testing plans under multiple-stress-type

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  • Elsayed, E.A.
  • Zhang, Hao

Abstract

Accelerated life testing (ALT) is used to obtain failure time data quickly under high stress levels in order to predict product life performance under design stress conditions. Most of the previous work on designing ALT plans is focused on the application of a single stress. However, as components or products become more reliable due to technological advances, it becomes more difficult to obtain significant amount of failure data within reasonable amount of time using single stress only. Multiple-stress-type ALTs have been employed as a means of overcoming such difficulties. In this paper, we design optimum multiple-stress-type ALT plans based on the proportional hazards model. The optimum combinations of stresses and their levels are determined such that the variance of the reliability estimate of the product over a specified period of time is minimized. The use of the model is illustrated using numerical example, and sensitivity analysis shows that the resultant optimum ALT plan is robust to the deviation in model parameters.

Suggested Citation

  • Elsayed, E.A. & Zhang, Hao, 2007. "Design of PH-based accelerated life testing plans under multiple-stress-type," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 286-292.
    • Handle: RePEc:eee:reensy:v:92:y:2007:i:3:p:286-292
    DOI: 10.1016/j.ress.2006.04.016
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    References listed on IDEAS

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    1. Harald Hannerz, 2001. "An extension of relational methods in mortality estimations," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 4(10), pages 337-368.
    2. Jung‐Won Park & Bong‐Jin Yum, 1996. "Optimal design of accelerated life tests with two stresses," Naval Research Logistics (NRL), John Wiley & Sons, vol. 43(6), pages 863-884, September.
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    Citations

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    Cited by:

    1. Haghighi, Firoozeh, 2014. "Optimal design of accelerated life tests for an extension of the exponential distribution," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 251-256.
    2. Ekene Gabriel Okafor & Whit Vinson & David Ryan Huitink, 2023. "Effect of Stress Interaction on Multi-Stress Accelerated Life Test Plan: Assessment Based on Particle Swarm Optimization," Sustainability, MDPI, vol. 15(4), pages 1-26, February.
    3. Yin, Yi-Chao & Coolen, Frank P.A. & Coolen-Maturi, Tahani, 2017. "An imprecise statistical method for accelerated life testing using the power-Weibull model," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 158-167.
    4. Compare, M. & Baraldi, P. & Bani, I. & Zio, E. & McDonnell, D., 2020. "Industrial equipment reliability estimation: A Bayesian Weibull regression model with covariate selection," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    5. Ao, Dan & Hu, Zhen & Mahadevan, Sankaran, 2017. "Design of validation experiments for life prediction models," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 22-33.
    6. Finkelstein, Maxim, 2013. "On dependent items in series in different environments," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 119-122.
    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. Qin, Shuidan & Wang, Bing Xing & Wu, Wenhui & Ma, Chao, 2022. "The prediction intervals of remaining useful life based on constant stress accelerated life test data," European Journal of Operational Research, Elsevier, vol. 301(2), pages 747-755.
    9. Volf, P. & Timková, J., 2014. "On selection of optimal stochastic model for accelerated life testing," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 291-297.
    10. Traore, M. & Chammas, A. & Duviella, E., 2015. "Supervision and prognosis architecture based on dynamical classification method for the predictive maintenance of dynamical evolving systems," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 120-131.
    11. Moustafa, Kassem & Hu, Zhen & Mourelatos, Zissimos P. & Baseski, Igor & Majcher, Monica, 2021. "System reliability analysis using component-level and system-level accelerated life testing," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    12. Zhaojun Yang & Xiaoxu Li & Chuanhai Chen & Hongxun Zhao & Dingyu Yang & Jinyan Guo & Wei Luo, 2019. "Reliability assessment of the spindle systems with a competing risk model," Journal of Risk and Reliability, , vol. 233(2), pages 226-234, April.
    13. Han, David, 2015. "Time and cost constrained optimal designs of constant-stress and step-stress accelerated life tests," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 1-14.
    14. Yao Liu & Yashun Wang & Zhengwei Fan & Xun Chen & Chunhua Zhang & Yuanyuan Tan, 2020. "A new universal multi-stress acceleration model and multi-parameter estimation method based on particle swarm optimization," Journal of Risk and Reliability, , vol. 234(6), pages 764-778, December.
    15. Abdullah AH Ahmadini & Frank PA Coolen, 2020. "Statistical inference for the Arrhenius-Weibull accelerated life testing model with imprecision based on the likelihood ratio test," Journal of Risk and Reliability, , vol. 234(2), pages 275-289, April.
    16. Tian, Zhigang & Liao, Haitao, 2011. "Condition based maintenance optimization for multi-component systems using proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 581-589.

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