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A Bayesian reliability evaluation method with integrated accelerated degradation testing and field information

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  • Wang, Lizhi
  • Pan, Rong
  • Li, Xiaoyang
  • Jiang, Tongmin

Abstract

Accelerated degradation testing (ADT) is a common approach in reliability prediction, especially for products with high reliability. However, oftentimes the laboratory condition of ADT is different from the field condition; thus, to predict field failure, one need to calibrate the prediction made by using ADT data. In this paper a Bayesian evaluation method is proposed to integrate the ADT data from laboratory with the failure data from field. Calibration factors are introduced to calibrate the difference between the lab and the field conditions so as to predict a product's actual field reliability more accurately. The information fusion and statistical inference procedure are carried out through a Bayesian approach and Markov chain Monte Carlo methods. The proposed method is demonstrated by two examples and the sensitivity analysis to prior distribution assumption.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:112:y:2013:i:c:p:38-47
    DOI: 10.1016/j.ress.2012.09.015
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    References listed on IDEAS

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    Cited by:

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    12. 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.
    13. Le Liu & Xiao-Yang Li & Enrico Zio & Rui Kang & Tong-Min Jiang, 2017. "Model Uncertainty in Accelerated Degradation Testing Analysis," Post-Print hal-01652218, HAL.
    14. Dejing Kong & Lirong Cui, 2016. "Bayesian inference of multi-stage reliability for degradation systems with calibrations," Journal of Risk and Reliability, , vol. 230(1), pages 18-33, February.
    15. Liu, Di & Wang, Shaoping, 2021. "Reliability estimation from lifetime testing data and degradation testing data with measurement error based on evidential variable and Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
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    20. 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.

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