Advancing ECLSS Reliability Modeling: Integrating ISS Data for Sustainable Long-Duration Mission Planning

The performance of ECLSS technology has been a popular when assessing overall system maturity. Most studies have examined either the component or the system-of-systems level. However, one crucial aspect often overlooked is sustainability with limited studies addressing long-term operational efficiency and resource management. The last release of ECLSS reliability data in 2004 created a significant gap in understanding the current reliability and maintainability of today's systems, especially considering advancements in technology and mission requirements since then. This lack of updated data hinders the development of more sustainable and resilient life support systems, essential for long-duration missions such as those planned for the LEO commercial stations, Moon and Mars. One of the most well-established reliability models is Langley Research Center’s Exploration Maintainability Analysis Tool. EMAT simulates daily operations over a predefined mission timeline by stochastically triggering component failures, executing repair actions, and tracking the required spare parts. While instrumental in understanding long-term mission demands, it primarily focuses on component reliability and maintainability. The later development of a supportability module using a Semi-Markov Process-based algorithm further enhanced sparing prediction for selected ECLS subsystems. However, this approach was only applied at the subsystem level, and its scalability to the system-of-systems level has not been fully explored. As system complexity increases, applying SMP becomes more challenging, requiring numerous assumptions and a greater modeling effort, limiting its scalability. This paper aims to advance reliability modeling by incorporating ISS data on ECLSS component replacement rates, shelf life, and throughput limits. By integrating these factors, the study seeks to create a comprehensive reliability model that accounts for all potential variables. This model will provide insights into sparing requirements for long-duration missions, enabling mission planners to better anticipate and manage the logistics of ECLSS maintenance, ultimately contributing to a more sustainable and reliable system for future space exploration.