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首页> 外文期刊>Reliability Engineering & System Safety >Bayesian model averaging based reliability analysis method for monotonic degradation dataset based on inverse Gaussian process and Gamma process
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Bayesian model averaging based reliability analysis method for monotonic degradation dataset based on inverse Gaussian process and Gamma process

机译：基于贝叶斯模型平均的基于逆高斯过程和伽玛过程的单调退化数据集可靠性分析方法

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摘要

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.
机译：提出了一种基于贝叶斯模型平均的可靠性分析方法，用于单调降级建模和推理。考虑到模型的不确定性，应用贝叶斯模型平均法来组合候选单调过程，特别是伽玛过程和高斯逆过程。为了评估总体可靠性，突出显示了产品总体中单位之间的差异和异质性，因此考虑了模型参数和模型概率的随机影响。完全贝叶斯推断被应用于估计分布超参数，其中先验是通过矩估计与最大似然估计相结合而获得的。使用先前发布的GaAs激光退化数据集验证了所提出的基于贝叶斯模型平均的可靠性分析方法。结果表明，所提出的基于贝叶斯模型平均的方法在评估总体可靠性时提供了灵活性。

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