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Bayesian model averaging based reliability analysis method for monotonic degradation dataset based on inverse Gaussian process and Gamma process

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  • Liu, Di
  • Wang, Shaoping
  • Zhang, Chao
  • Tomovic, Mileta

Abstract

A Bayesian model averaging based reliability analysis method for monotonic degradation modeling and inference is proposed in this paper. Considering the model uncertainty, the Bayesian model averaging method is applied to combine the candidate monotonic processes, specifically the Gamma process and inverse Gaussian process. To evaluate the population reliability, the unit-to-unit variations and heterogeneities within product population are highlighted, so the random effects of both the model parameters and model probabilities are taken in to account. The fully Bayesian inference is applied to estimate distribution hyper-parameters, in which the priors are obtained by moment estimation combined with maximum-likelihood estimation. The proposed Bayesian model averaging based reliability analysis method is verified using previously published GaAs laser degradation dataset. The results indicate that the proposed Bayesian model averaging based method provides flexibility when evaluating the population reliability.

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  • 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.
    • Handle: RePEc:eee:reensy:v:180:y:2018:i:c:p:25-38
    DOI: 10.1016/j.ress.2018.06.019
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    1. 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.
    2. Bae, Suk Joo & Kuo, Way & Kvam, Paul H., 2007. "Degradation models and implied lifetime distributions," Reliability Engineering and System Safety, Elsevier, vol. 92(5), pages 601-608.
    3. Kabir, Golam & Tesfamariam, Solomon & Sadiq, Rehan, 2015. "Predicting water main failures using Bayesian model averaging and survival modelling approach," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 498-514.
    4. Enrique López Droguett & Ali Mosleh, 2008. "Bayesian Methodology for Model Uncertainty Using Model Performance Data," Risk Analysis, John Wiley & Sons, vol. 28(5), pages 1457-1476, October.
    5. Riley, Matthew E., 2015. "Evidence-based quantification of uncertainties induced via simulation-based modeling," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 79-86.
    6. Zhi‐Sheng Ye & Min Xie, 2015. "Rejoinder to ‘Stochastic modelling and analysis of degradation for highly reliable products’," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(1), pages 35-36, January.
    7. Park, Inseok & Grandhi, Ramana V., 2014. "A Bayesian statistical method for quantifying model form uncertainty and two model combination methods," Reliability Engineering and System Safety, Elsevier, vol. 129(C), pages 46-56.
    8. 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.
    9. Wu, Jianing & Yan, Shaoze & Zuo, Ming J., 2016. "Evaluating the reliability of multi-body mechanisms: A method considering the uncertainties of dynamic performance," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 96-106.
    10. Lu, Zhong & Liang, Xihui & Zuo, Ming J. & Zhou, Jia, 2017. "Markov process based time limited dispatch analysis with constraints of both dispatch reliability and average safety levels," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 84-94.
    11. Zhi‐Sheng Ye & Min Xie, 2015. "Stochastic modelling and analysis of degradation for highly reliable products," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(1), pages 16-32, January.
    12. 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.
    13. Ye, Zhi-Sheng & Chen, Nan & Shen, Yan, 2015. "A new class of Wiener process models for degradation analysis," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 58-67.
    14. Park, Inseok & Amarchinta, Hemanth K. & Grandhi, Ramana V., 2010. "A Bayesian approach for quantification of model uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 777-785.
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