结构结果分析:迈向系统架构设计中组件结果重要性的量化

Hannah S. Walsh, Mohammad Hejase, Daniel E. Hulse, G. Brat, I. Tumer
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引用次数: 0

摘要

在安全关键型系统中,有一个主要的推动力是在设计过程的早期考虑系统风险,以避免后来昂贵的重新设计。然而,现有的技术可能是劳动密集型的,并且受到许多不确定性来源的影响,依赖于故障模式和故障率数据,这些数据只能在早期设计阶段进行估计。本文提出了一种基于网络的技术,用于评估特定组件的相应重要性,使设计人员能够在不使用估计故障率的情况下考虑系统架构设计中的危险。结构后果分析表示具有网络的组件之间的连通性,并提供风险预防和缓解技术(如冗余)的显式表示。该网络增加了“端”组件或接收器故障后果的度量,可以通过网络反向传播,以计算与所有组件故障相关的后果。基于这一结果,设计人员可以考虑缓解策略,例如冗余或提高组件可靠性。该方法在某无人机副翼电子控制系统的设计中得到了验证。研究发现,结构后果分析可以在没有故障率数据的情况下识别潜在的重要部件,使设计师能够在设计过程的早期主动进行风险设计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structural Consequence Analysis: Towards the Quantification of Component Consequential Importance in System Architecture Design
There is a major push in safety-critical systems to consider system risk early in the design process in order to avoid costly redesign later on. However, existing techniques, which may be labor-intensive and be subject to many sources of uncertainty, rely on failure mode and failure rate data, which can only be estimated in the early design phase. This paper proposes a network-based technique for assessing the consequential importance of a particular component to enable designers to consider hazards in the design of the system architecture without the use of estimated failure rates. Structural consequence analysis represents connectivity between components with a network and provides an explicit representation of risk prevention and mitigation techniques, such as redundancy. The network is augmented with a measure of the consequence of the failure of the “end” components, or sinks, which can be backpropagated through the network to compute the consequence associated with the failure of all components. Based on this consequence, designers can consider mitigation strategies, such as redundancy or increased component reliability. The approach is demonstrated in the design of an electric system to control an aileron of an unmanned aircraft system (UAS). It is found that structural consequence analysis can identify potentially important components without failure rate data, allowing designers to proactively design for risk earlier in the design process.
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