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A Stochastic Approach for the Analysis of Dynamic Fault Trees With Spare Gates Under Probabilistic Common Cause Failures

机译:概率共因故障下带有备用门的动态故障树分析的随机方法

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摘要

A redundant system usually consists of primary and standby modules. The so-called spare gate is extensively used to model the dynamic behavior of redundant systems in the application of dynamic fault trees (DFTs). Several methodologies have been proposed to evaluate the reliability of DFTs containing spare gates by computing the failure probability. However, either a complex analysis or significant simulation time are usually required by such an approach. Moreover, it is difficult to compute the failure probability of a system with component failures that are not exponentially distributed. Additionally, probabilistic common cause failures (PCCFs) have been widely reported, usually occurring in a statistically dependent manner. Failure to account for the effect of PCCFs overestimates the reliability of a DFT. In this paper, stochastic computational models are proposed for an efficient analysis of spare gates and PCCFs in a DFT. Using these models, a DFT with spare gates under PCCFs can be efficiently evaluated. In the proposed stochastic approach, a signal probability is encoded as a non-Bernoulli sequence of random permutations of fixed numbers of ones and zeros. The component's failure probability is not limited to an exponential distribution, thus this approach is applicable to a DFT analysis in a general case. Several case studies are evaluated to show the accuracy and efficiency of the proposed approach, compared to both an analytical approach and Monte Carlo (MC) simulation.
机译:冗余系统通常由主模块和备用模块组成。所谓的备用门广泛用于在动态故障树(DFT)应用中对冗余系统的动态行为进行建模。已经提出了几种方法来通过计算故障概率来评估包含备用门的DFT的可靠性。但是,这种方法通常需要复杂的分析或大量的仿真时间。此外,很难计算出组件故障没有按指数分布的系统的故障概率。另外,已经广泛报道了概率共因故障(PCCF),通常以统计相关的方式发生。无法考虑PCCF的影响会高估DFT的可靠性。本文提出了一种随机计算模型,用于对DFT中的备用门和PCCF进行有效分析。使用这些模型,可以有效地评估PCCF下带有备用门的DFT。在提出的随机方法中,信号概率被编码为固定数目的1和0的随机排列的非伯努利序列。组件的故障概率不限于指数分布,因此该方法适用于一般情况下的DFT分析。与分析方法和蒙特卡洛(MC)仿真相比,对几个案例研究进行了评估,以显示所提出方法的准确性和效率。

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