累积损伤模型的不确定失效阈值

A. Usynin, J. Hines, A. Urmanov
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引用次数: 18

摘要

本文研究了基于退化的可靠性模型中可变性的相关问题,以及可变性如何影响这些模型所做的剩余使用寿命预测。特别是,累积损伤模型中的不确定失效阈值是本研究的主要兴趣。许多基于退化的可靠性方法使用故障阈值的预定义确定性值。然而,在实际情况下,设计者可能不知道精确的临界退化水平。在这种情况下,将临界退化水平定义为具有一定临界概率的值范围是合适的。如果没有关于故障阈值的先验信息;临界值必须从实验可靠性数据中估计,这些数据由于测量不完善和被测部件可靠性特性的随机偏差而具有不确定性。在这些情况下,将临界阈值建模为随机变量是可取的。否则,由于忽略了失效阈值不确定性,模型可能会过于简化,而失效阈值不确定性对可靠性预测的影响是显著的。本文结合累积损伤模型,对失效阈值变化对可靠性预测的影响进行了不确定性分析。研究了三种累积损伤模型;它们是基于马尔可夫链的模型、线性路径退化模型和带漂移的维纳过程。给出了将阈值不确定性传递到模型预测中的闭式方程。给出了一个数值算例,说明临界阈值不确定性如何影响预测的失效时间分布,支持在精确可靠性计算中考虑临界阈值不确定性的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uncertain failure thresholds in cumulative damage models
This paper investigates the issues related to variability in degradation-based reliability models and how the variability affects the remaining useful life prognosis being made by those models. Particularly, uncertain failure thresholds in cumulative damage models are of primary interest in this study. Many degradation-based reliability approaches make use of a predefined deterministic value of the failure threshold. However, in real-world cases, the designer may not be aware of the precise critical degradation level. In such situations it is suitable to define the critical degradation level as a range of values having certain probabilities of being critical. If no prior information is available regarding the failure threshold; the critical value has to be estimated from experimental reliability data that are subject to uncertainty due to imperfect measurements and random deviations in reliability properties of the tested components. In these circumstances, it is desirable to model the critical threshold as a random variable. Otherwise, the model can be oversimplified since it neglects the failure threshold uncertainty, whose influence onto the reliability prediction can be significant. This paper presents uncertainty analysis regarding how variability in the failure threshold affects the reliability prediction in conjunction with cumulative damage models. Three types of cumulative damage models are investigated; these are a Markov chain-based model, a linear path degradation model, and a Wiener process with drift. Closed-form equations quantifying the threshold uncertainty propagation into the model prediction are given. A numerical example is presented to illustrate how the critical threshold uncertainty reshapes the predicted time-to-failure distribution, supporting the need for considering the critical threshold uncertainty in accurate reliability computations.
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