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A general Bayes weibull inference model for accelerated life testing

Author

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  • René Van Dorp, J.
  • Mazzuchi, Thomas A.

Abstract

This article presents the development of a general Bayes inference model for accelerated life testing. The failure times at a constant stress level are assumed to belong to a Weibull distribution, but the specification of strict adherence to a parametric time-transformation function is not required. Rather, prior information is used to indirectly define a multivariate prior distribution for the scale parameters at the various stress levels and the common shape parameter. Using the approach, Bayes point estimates as well as probability statements for use-stress (and accelerated) life parameters may be inferred from a host of testing scenarios. The inference procedure accommodates both the interval data sampling strategy and type I censored sampling strategy for the collection of ALT test data. The inference procedure uses the well-known MCMC (Markov Chain Monte Carlo) methods to derive posterior approximations. The approach is illustrated with an example.

Suggested Citation

  • René Van Dorp, J. & Mazzuchi, Thomas A., 2005. "A general Bayes weibull inference model for accelerated life testing," Reliability Engineering and System Safety, Elsevier, vol. 90(2), pages 140-147.
    • Handle: RePEc:eee:reensy:v:90:y:2005:i:2:p:140-147
    DOI: 10.1016/j.ress.2004.10.012
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    Citations

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    Cited by:

    1. Han, David & Bai, Tianyu, 2020. "Design optimization of a simple step-stress accelerated life test – Contrast between continuous and interval inspections with non-uniform step durations," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    2. Finkelstein, Maxim, 2013. "On dependent items in series in different environments," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 119-122.
    3. Maria Kateri & Udo Kamps, 2015. "Inference in step-stress models based on failure rates," Statistical Papers, Springer, vol. 56(3), pages 639-660, August.
    4. 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.
    5. 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.
    6. 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).
    7. 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.
    8. Quigley, John & Walls, Lesley, 2011. "Mixing Bayes and empirical Bayes inference to anticipate the realization of engineering concerns about variant system designs," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 933-941.
    9. 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.

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