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Quantitative Fault Tree Analyses (QFTA) are a fundamental tool for determining residual risk and are used in hazard control in many industries. In addition to assessing the probabilities of hazards after control measures have been implemented, they provide a fundamental aid to Risk Informed Decision Making (RIDM), by highlighting the weaknesses in a design and allowing efficient application of countermeasures. In the space sector, QFTAs are currently required for every manned spaceflight mission. However, the effectiveness of the quantitative aspect is highly dependent on the numerical methods used for its implementation, especially when it comes to Common Cause Failures (CCF). CCFs have the potential to negate the benefits of redundancy; they must therefore be properly addressed for effective system improvement. Although many methodologies have been implemented to address CCFs from a quantitative point of view, a space-specific methodology requires proper investigation and concrete procedures. This paper offers a summary of methods for the quantitative assessment of CCF effects on QFTAs from different industrial sectors, referring to many available standards and guides, and then discusses their applicability to the space domain. After identifying the constraints imposed by Space Engineering and stating the requirements for an effective industrial approach, the paper will discuss the CCF methodologies for manned space stations in the context of the Lunar Gateway's Probabilistic Risk Assessment. The work focuses on discussing the most relevant CCF methodologies from the literature, and proposing innovative solutions where necessary. The definition of CCF is first addressed, followed by the tactics for identifying Common Cause Component Groups - CCCG (which are not limited to redundant configurations), the process for root causes identification, and their classification. Emphasis is then put on the definition of a method for the practical implementation of the CCFs in QFTAs of space hazards. Discussions on the feasibility and effectiveness of implicit and explicit methods are proposed. The applicability of innovative methods, such as a hybrid implicit/explicit approach is discussed. Finally, clear recommendations for the quantification and control of the risks associated with CCFs are given.
Giugliarelli et al. (Sat,) studied this question.