Approach for Inferring Fractiles of Future Time between Failures

R. Jiang
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引用次数: 1

Abstract

When the data on the time between failures (TBF) are available, a challenging issue is to infer the distribution of future TBFs. The existing approaches to address this issue include varying-parameter normal and Weibull distributions, where the distributional parameters are functions of the number of cumulative failures. Since the distributional parameters are extrapolated from the two fitted models of the distribution parameters, these approaches may be not robust. In this paper, we propose an improved approach. The proposed approach first estimates alpha-fractiles of time to failure from the observed data for multiple alpha values, and then fits the estimates associated with each alpha value to a three-parameter power-law model. The fitted power-law models are used to estimate the fractiles of a certain future TBF, which form an empirical distribution of the future TBF. The empirical distribution can be further fitted to a distribution model. Due to multiple fractiles are estimated, it is expected that the proposed approach is robust. The approach is illustrated by the well-known bus-motor failure data.
一种推断故障间未来时间粒子的方法
当故障间隔时间(TBF)数据可用时,一个具有挑战性的问题是推断未来TBF的分布。解决这一问题的现有方法包括变参数正态分布和威布尔分布,其中分布参数是累积故障数量的函数。由于分布参数是从分布参数的两个拟合模型中推断出来的,因此这些方法可能不太稳健。在本文中,我们提出了一个改进的方法。该方法首先从多个α值的观测数据中估计到故障的α -粒子数,然后将与每个α值相关的估计拟合到三参数幂律模型中。拟合的幂律模型用于估计某一未来TBF的粒子数,从而形成未来TBF的经验分布。经验分布可以进一步拟合到分布模型中。由于估计了多个粒子,因此期望该方法具有鲁棒性。该方法以众所周知的母线电机故障数据为例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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