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Reliability analysis based on combination of universal generating function and discrete approach

机译:基于通用生成函数和离散方法相结合的可靠性分析

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Uncertainty exists in the engineering practices widely. Since a multidimensional integration problem should be dealt with during the process of reliability-based analysis and design, it is the key problem to develop new method to improve the efficiency and accuracy for the reliability-based analysis and design in the complex systems. Hence a new method is proposed, and the procedure of the proposed method is summarized as follows. First, transform continuous random variable into discrete random variables modeled by probability mass function (PMF). The PMF of a limit-state function can be acquired through universal generating function (UGF) and different moments can be calculated by using derivative. Second, maximum entropy principle is used to calculate the probability density function (PDF) of the limit-state function. The proposed method, based on the PMF and UGF, is suitable for the cases that discrete variables exist in the system and the limit-state function is a highly non-linear problem. The reason is that the proposed method needs neither derivative nor the most probable point (MPP) search. A numerical example is provided to demonstrate the effectiveness of the proposed method, and furthermore a comparison is made between the results from the proposed method and Monte Carlo simulation (MCS).
机译:广泛存在的工程实践存在不确定性。由于在基于可靠性的分析和设计过程中应处理多维集成问题,因此开发新方法的关键问题,以提高复杂系统中基于可靠性的分析和设计的效率和准确性。因此,提出了一种新方法,并且所提出的方法的程序总结如下。首先,将连续随机变量转换为由概率质量函数(PMF)建模的离散随机变量。可以通过通用生成函数(UGF)获取限制状态函数的PMF,并且可以通过使用衍生来计算不同的时刻。其次,最大熵原理用于计算极限状态函数的概率密度函数(PDF)。基于PMF和UGF的所提出的方法适用于系统中存在离散变量的情况,并且限制状态函数是高度线性问题。原因是所提出的方法既不需要衍生性,也不需要最可能的点(MPP)搜索。提供了一个数值示例以证明所提出的方法的有效性,此外,在所提出的方法和蒙特卡罗模拟(MCS)之间的结果之间进行比较。

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