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Spark-Based Monte Carlo Parallel Simulation Approach for FTA

机译：基于Spark的蒙特卡罗平行模拟方法

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摘要

In the field of reliability and safety, Fault-tree analysis proved to be a useful analytic tool. Aimed at static or dynamic FTs, Monte Carlo simulation based on time-to-failure (TTF) may be an effective way to calculate FTs. Through computing the TTF of bottom events of FTs, the TTF of the system can be calculated easily. However, Monte Carlo simulation based on TTF is time-consuming because modern engineering systems have become more complex and larger in scale. Traditional computation framework, in individual computers or small scale computer cluster, obviously cannot undertake this tough task. To address this issue, this paper puts forward a parallel algorithm under the Spark parallel computing framework, and through the experiment proves that the parallel algorithm can quickly get the simulation results and have the advantage of calculating large-scale dynamic FTs. This is very meaningful in the engineering.
机译：在可靠性和安全领域，故障树分析被证明是一个有用的分析工具。针对静态或动态FTS，基于故障时间（TTF）的Monte Carlo仿真可能是计算FTS的有效方法。通过计算FTS的底部事件的TTF，可以容易地计算系统的TTF。然而，基于TTF的Monte Carlo仿真是耗时的，因为现代工程系统的规模变得更加复杂，更大。传统的计算框架，在各个计算机或小型计算机集群中，显然无法进行这项艰巨的任务。为了解决这个问题，本文提出了在火花并行计算框架下的并行算法，并通过实验证明并行算法可以快速获取仿真结果并具有计算大规模动态FTS的优势。这在工程中非常有意义。

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