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Approximate Reliability Evaluation of Large-Scale Multistate Series-Parallel Systems

机译:大型多状态串并联系统的近似可靠性评估

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摘要

Multistate series-parallel system (MSSPS) is a widely used model for representing engineering systems, whose reliability has been extensively analyzed. Universal generating function (UGF) is an efficient method for evaluating the reliability of MSSPS. However, when facing the large-scale MSSPS, where the number of system components and possible states are enormous, calclating the exact system reliability can be rather time-consuming. To evaluate the reliability of large-scale MSSPS more efficiently, this paper proposes an approximation method, named continuization discretization approximation (CDA) method. The CDA approach consists of continuization and discretization processes. The continuization process applies Gaussian approximation method based on the central limit theory and the UGF technique to evaluate parallel subsystems. While the discretization process discretizes the continuous distribution to a discrete one, and proposes an algorithm to evaluate the series subsystems efficiently. The efficiency and accuracy performance of the CDA method can be adjusted by parameters according to the computational resource and the system scale. The newly proposed method is compared to the existing methods in evaluating the large-scale MSSPS. Numerical examples show that the CDA method has evident advantage in computational efficiency with satisfactory accuracy performance.
机译:多状态串并联系统(MSSPS)是广泛用于表示工程系统的模型,其可靠性已得到广泛分析。通用生成函数(UGF)是评估MSSPS可靠性的有效方法。但是,当面对大型MSSPS时,系统组件的数量和可能的状态非常多,计算准确的系统可靠性可能会非常耗时。为了更有效地评估大规模MSSPS的可靠性,本文提出了一种近似方法,称为连续离散近似(CDA)方法。 CDA方法由连续化和离散化过程组成。连续化过程采用基于中心极限理论和UGF技术的高斯近似方法来评估并行子系统。离散化过程将连续分布离散化为离散分布,并提出了一种算法来有效评估系列子系统。 CDA方法的效率和准确性可以根据计算资源和系统规模通过参数进行调整。将新提出的方法与现有方法进行大规模MSSPS评估。数值算例表明,CDA方法在计算效率上具有明显的优势,并且具有令人满意的精度性能。

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