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Bayesian and likelihood inferences on remaining useful life in two-phase degradation models under gamma process

机译：伽马过程两相降解模型中剩余使用寿命的贝叶斯和似然推断

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Remaining useful life prediction has been one of the important research topics in reliability engineering. For modern products, due to physical and chemical changes that take place with usage and with age, a significant degradation rate change usually exists. Degradation models that do not incorporate a change point may not accurately predict the remaining useful life of products with two-phase degradation. For this reason, we consider the degradation analysis for products with two-phase degradation under gamma processes. Incorporating a probability distribution of the time at which the degradation rate changes into the degradation model, the remaining useful life prediction for a single product can be obtained, even though the rate change has not occurred during the inspection. A Bayesian approach and a likelihood approach via stochastic expectation-maximization algorithm are proposed for the statistical inference of the remaining useful life. A simulation study is carried out to evaluate the performance of the developed methodologies to the remaining useful life prediction. Our results show that the likelihood approach yields relatively less bias and more reliable interval estimates, while the Bayesian approach requires less computational time. Finally, a real dataset on LEDs is presented to demonstrate an application of the proposed methodologies. (C) 2017 Elsevier Ltd. All rights reserved.
机译：剩余使用寿命预测一直是可靠性工程中的重要研究主题之一。对于现代产品，由于随着使用时间和年龄而发生的物理和化学变化，通常存在明显的降解率变化​​。未包含变化点的退化模型可能无法准确预测具有两相退化的产品的剩余使用寿命。因此，我们考虑对伽马过程中具有两相降解的产品进行降解分析。将降解率发生变化的时间的概率分布纳入降解模型，即使在检查过程中未发生率变化，也可以获得单个产品的剩余使用寿命预测。针对剩余使用寿命的统计推断，提出了一种基于贝叶斯方法和一种基于随机期望最大化算法的似然方法。进行了仿真研究，以评估所开发方法对剩余使用寿命的预测的性能。我们的结果表明，似然方法产生的偏差相对较小，区间估计更可靠，而贝叶斯方法则需要较少的计算时间。最后，提供了一个有关LED的真实数据集，以演示所提出方法的应用。 （C）2017 Elsevier Ltd.保留所有权利。

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