Design for Space Architecture Maintainability: Applications of Quantitative and Qualitative Metrics

Maintainability of space systems—the ease and rapidity with which systems can be preserved against failure or restored to operational status—has often not been a primary design consideration and has consistently remained an issue, frequently addressed too late in design cycles to be fully considered. Design for Maintainability (DfM) is a strategy that encompasses principles and measures implemented during planning and design to ensure ease, safety, and cost-effectiveness in maintenance. DfM is particularly critical in extraterrestrial settings, where space architecture and deep space systems must rely on onboard maintenance with little to no ground support. Aiming to examine the applicability of DfM in space architecture and explore its path forward, this paper outlines the scope of maintenance activities in space architecture and reviews both qualitative and quantitative maintainability metrics in the literature. Based on a consolidated list of qualitative maintainability attributes identified across domains, this paper presents examples of design concepts and features embodying these attributes, demonstrating their applicability to space architecture through an illustrative surface habitat design concept. The review of quantitative maintainability indicators underscores the challenges of applying such metrics at early stages of design for novel and unprecedented systems. This paper further discusses the synergy and conflict among these attributes, an area with limited discussion in the literature, and explores examples of both synergistic and conflicting relationships, as well as their nuanced interplays depending on the design approach. In addition to advancing the effort of implementing integrated design processes that employ quantitative metrics across teams and organizations for trade studies in early design phases, this paper concludes by emphasizing the integration of qualitative maintainability attributes into system development processes, backed by the potential of data-based design recommendations and matrix-based methods for mapping attributes to functional requirements and design features, resolving conflicts, and facilitating synergy among them.