美国国家航空航天局太空发射系统故障管理算法风险评估

William A. Maul, Yunnhon Lo, Edmond Wong
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摘要

本文介绍了目前正在对太空发射系统(SLS)Artemis II 故障管理(FM)检测功能进行的假阳性(FP)和假阴性(FN)风险评估过程。由于最初的分析表明软件和固件故障在总风险中占主导地位,因此努力对这些风险进行细化,其中包括- 为每种检测算法建立软件功能跟踪, - 利用逻辑源代码行数(LSLOC), - 改进软件故障率,以及 - 在适用的单个故障树中为常见的硬件和软件故障模式建立分数乘数。还讨论了这些工作及其对总体分析的影响。对分析范围、一般假设和指导规则以及关键建模概念进行了讨论,以建立风险评估的基础。即使实施了分析改进,软件和固件仍然是造成风险的主要因素,但硬件故障(主要以常见故障(CCF)的形式出现)也被视为风险驱动因素。通过改进,可以对单个检测功能和整个调频套件进行风险评估。在软件风险评估中如何考虑时间和冗余问题仍然是今后工作的重点。
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Fault Management Algorithm Risk Assessment for the NASA Space Launch System
This paper presents the false positive (FP) and false negative (FN) risk assessment process currently being conducted for the Space Launch System (SLS) Artemis II Fault Management (FM) detection functions. Because initial analyses indicated a dominance in the total risk by software and firmware failures, efforts were made to refine those risks which involved: • Establishing software function traces for each detection algorithm, • Utilizing the Logical Source Lines of Code (LSLOC) count, • Refinement of the software failure rate, and • Establishing fractional multipliers for common hardware and software failure modes across the applicable individual fault trees. These efforts and their impact on the overall analyses are also discussed. The analysis scope, general assumptions and guide rules, and key modeling concepts are discussed to establish the basis of the risk assessments conducted. Even with the implementation of the analysis refinements, software and firmware are still key risk contributors, but hardware failures, primarily in the form of Common Cause Failures (CCFs), are also indicated as risk drivers. The refinements enable risk estimations of individual detection functions as well as the entire FM suite. There still remains issues of how to account for time and redundancy in the software risk estimations that will continue to be the focus of future work.
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