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Efficient Verification and Validation of Performance-Based Safety Requirements using Subset Simulation

机译:使用子集仿真有效验证和验证基于性能的安全要求

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The safety and performance of aircraft systems must fulfill stringent requirements defined by certification authorities. Performance-based requirements are specified with a probability threshold. The probability of failure of the performance of the system must be below the threshold. Depending on the severity of the outcome, the probability thresholds are typically of the order 10~(-5)(less severe) to 10~(-9)(catastrophic result in loss of life). Estimation of such low probabilities using naive Direct Monte Carlo methods generates a significant computational load that increases development costs. This paper presents a Markov chain Monte Carlo toolchain concept that uses the Subset Simulation algorithm to efficiently estimate low probability of failure of performance-based requirements. The Subset Simulation algorithm targets the rare region of interest of the overall probability distribution to realize the probability of failure efficiently. Example scenarios consisting of estimating the accuracy requirement of an Air Data, Attitude and Heading Reference System and closed-loop hover requirements of an electric Vertical Take-Off and Landing aircraft demonstrate the application of the toolchain. More importantly, the samples generated during subset simulation are used to identify key parameters that significantly influence the failure of the requirement. Such information is very useful throughout the development process.
机译:飞机系统的安全性和性能必须满足认证机构定义的严格要求。基于性能的要求指定具有概率阈值。系统性能失败的可能性必须低于阈值。根据结果​​的严重程度,概率阈值通常是10〜(-5)(不太严重)至10〜(-9)的顺序(灾难性导致生命损失)。使用Naive Direct Monte Carlo方法估计这种低概率的概率产生了显着的计算负载,提高了开发成本。本文介绍了马尔可夫链Monte Carlo Toolchain概念,它使用子集仿真算法有效地估计基于性能的性能失效的低概率。子集仿真算法针对整体概率分布的罕见地区,以有效地实现失败的可能性。示例场景包括估计空气数据的准确性要求,姿态和前线参考系统和电动垂直起飞和着陆飞机的闭环悬停要求证明了工具链的应用。更重要的是,子集模拟期间产生的样本用于识别显着影响要求失败的关键参数。在整个开发过程中,这些信息非常有用。

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