首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers, Part O. Journal of Risk and Reliability >Reliability analysis of series systems with multiple failure modes under epistemic and aleatory uncertainties
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Reliability analysis of series systems with multiple failure modes under epistemic and aleatory uncertainties

机译:认知和偶然不确定性下具有多种失效模式的串联系统的可靠性分析

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摘要

Uncertainty exists widely in engineering practice. An engineering system may have multiple failure criteria. In the current paper, system reliability analysis with multiple failure modes under both epistemic and aleatory uncertainties is presented. Epistemic uncertainty is modelled using p-boxes, while aleatory uncertainty is modelled using probability distributions. A first-order reliability method is developed and non-linear performance functions are linearized by the sampling method instead of the commonly used Taylor's expansion at the most probable point. Furthermore, multiple failure modes in a system are often correlated because they depend on the same uncertain variables. In order to consider these correlated failure modes, the methods proposed by Feng and Frank are extended in this paper in order to calculate the joint probability of failure for two arbitrary failure modes under both aleatory and epistemic uncertainties. The Pearson correlation coefficient of two arbitrary failure modes is determined by the sampling method. Since two types of uncertainty exist in the system, the probability of system failure is an interval rather than a point value. The probability of failure of the system can be obtained by the combination of the extension 'narrow' bound method and the interval arithmetic. A numerical example is presented to demonstrate the applicability of the proposed method.
机译:工程实践中普遍存在不确定性。工程系统可能具有多个故障准则。在本文中,提出了在认知和偶然不确定性下具有多种故障模式的系统可靠性分析。认知不确定性使用p盒建模,而偶然不确定性使用概率分布建模。开发了一阶可靠性方法,并通过采样方法而不是通常使用的泰勒展开在最可能的点上将非线性性能函数线性化。此外,系统中的多个故障模式通常相互关联,因为它们取决于相同的不确定变量。为了考虑这些相关的失效模式,本文扩展了Feng和Frank提出的方法,以计算在不确定性和认知不确定性下两种任意失效模式的联合失效概率。通过采样方法确定两种任意失效模式的皮尔逊相关系数。由于系统中存在两种类型的不确定性,因此系统故障的可能性是一个间隔而不是一个点值。系统的故障概率可以通过扩展“窄”界方法和区间算法的组合来获得。数值例子表明了该方法的适用性。

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