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Diagnostics in Birnbaum-Saunders accelerated life models with an application to fatigue data

机译:Birnbaum-Saunders 中的诊断技术加速了寿命模型,并应用于疲劳数据

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In industrial statistics, there is great interest in predicting with precision lifetimes of specimens that operate under stress. For example, a bad estimation of the lower percentiles of a life distribution can produce significant monetary losses to organizations due to an excessive amount of warranty claims. The Birnbaum-Saunders distribution is useful for modeling lifetime data. This is because such a distribution allows us to relate the total time until the failure occurs to some type of cumulative damage produced by stress. In this paper, we propose a methodology for detecting influence of atypical data in accelerated life models on the basis of the Birnbaum-Saunders distribution. The methodology developed in this study should be considered in the design of structures and in the prediction of warranty claims. We conclude this work with an application of the proposed methodology on the basis of real fatigue life data, which illustrates its importance in a warranty claim problem.
机译:在工业统计学中,人们非常重视精确预测在应力下运行的试样的寿命。例如,由于过多的保修索赔,对寿命分布的较低百分位数的错误估计可能会给组织带来重大的金钱损失。Birnbaum-Saunders 分布可用于对生命周期数据进行建模。这是因为这样的分布使我们能够将故障发生之前的总时间与应力产生的某种累积损伤联系起来。在本文中,我们提出了一种基于Birnbaum-Saunders分布的检测非典型数据对加速生命模型影响的方法。本研究中开发的方法应在结构设计和保修索赔预测中加以考虑。最后,我们基于真实的疲劳寿命数据应用了所提出的方法,说明了其在保修索赔问题中的重要性。

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