Lognormal Approximations of Fault Tree Uncertainty Distributions

Fault trees are used in reliability modeling to create logical models of fault combinations that can lead to undesirable events. The output of a fault tree analysis (the top event probability) is expressed in terms of the failure probabilities of basic events that are input to the model. Typically, the basic event probabilities are not known exactly, but are modeled as probability distributions: therefore, the top event probability is also represented as an uncertainty distribution. Monte Carlo methods are generally used for evaluating the uncertainty distribution, but such calculations are computationally intensive and do not readily reveal the dominant contributors to the uncertainty. In this article, a closed‐form approximation for the fault tree top event uncertainty distribution is developed, which is applicable when the uncertainties in the basic events of the model are lognormally distributed. The results of the approximate method are compared with results from two sampling‐based methods: namely, the Monte Carlo method and the Wilks method based on order statistics. It is shown that the closed‐form expression can provide a reasonable approximation to results obtained by Monte Carlo sampling, without incurring the computational expense. The Wilks method is found to be a useful means of providing an upper bound for the percentiles of the uncertainty distribution while being computationally inexpensive compared with full Monte Carlo sampling. The lognormal approximation method and Wilks’s method appear attractive, practical alternatives for the evaluation of uncertainty in the output of fault trees and similar multilinear models.     

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转子动不平衡载荷的在线识别

本文提出用复模态影响系数来解决转了大衡的在线以别和诊断问题，讨论了影响系数矩阵的力学意义．试验中可不加试重求出影响系数，并编制了相应的计算程序．     

大学英语阅读障碍诊断及治疗之探讨

由于阅读过程内在的复杂性，阅读是较难掌握的一项语言技能。本文从阅读障碍的诊断和治疗两方面对中国大学英语阅读教学进行探讨，抛砖引玉，以期有更多的同仁加入这一话题的讨论。     

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