ASTERIA

Lynntech proposes the development of advanced Fault Management (FM) Technologies for NASA's missions by leveraging contemporary Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance mission concepts, increase mission survivability, and alleviate the workload of FM engineers and mission operators. As NASA's missions grow in complexity and face tighter timetables and budget constraints, autonomous systems become imperative. To address both operational challenges and design complexities, Lynntech aims to utilize AI/ML for state estimation, fault diagnosis, and recovery strategies, alongside model-based system engineering (MBSE) and thus create advanced FM techniques that can significantly reduce the need for human intervention in spacecraft operations. Lynntech's approach encompasses addressing a wide array of hardware and software failures, sensor malfunctions, environmental interactions, and inter-subsystem fault propagation. This not only promises to increase spacecraft resilience and autonomy but also ensures uninterrupted scientific data collection. This effort also aims to accelerate the adoption of advanced FM techniques in future missions by significantly improving the understanding and implementation of FM early in mission planning and design thus leading to more reliable, cost-effective operations across a wide range of NASA missions. The technology can even have potential applications extending to launch vehicles and test stands. Deliverables include a detailed analysis, prototype development, and software showcasing the feasibility and commercial viability of the AI/ML-enhanced FM approaches. Success in Phase I will pave the way for Phase II prototype demonstrations and potential integration into NASA missions, contributing to increased mission reliability and autonomy in space exploration.
Benefits: Upon successful development and integration of AI/ML-enhanced Fault Management (FM) techniques, the proposed R&D has direct applications across a broad spectrum of NASA's missions, fostering advancements in spacecraft autonomy and resilience. This innovation is poised to transform the operational dynamics of both high-profile missions and smaller, high-risk exploratory endeavors, aligning with NASA’s strategic objectives of exploration and discovery. For large, complex missions with stringent reliability requirements, our FM approach ensures heightened system robustness against a myriad of potential faults, significantly minimizing the risk of mission failure. By automating the detection, diagnosis, and mitigation of system anomalies, these missions can achieve their ambitious objectives with reduced operational overhead and increased safety. In the domain of smaller, cost-sensitive missions, which are increasingly prevalent due to advances in microdevices and affordable space access, the innovation offers a paradigm shift. It equips these missions with the necessary autonomy to manage faults effectively under severe resource constraints, thus maximizing scientific yield and operational efficiency. Furthermore, our AI/ML-driven FM solutions have implications beyond spacecraft, extending to other high-value systems such as launch vehicles and test stands. The flexible, adaptable nature of the proposed FM techniques allows for their application across various mission profiles, ensuring broad relevance and utility within NASA’s portfolio. By advancing the state of FM technology, this project directly supports NASA's overarching goals of enhancing mission reliability, reducing costs, and enabling more ambitious explorations of our solar system and beyond, thereby contributing to the expansion of human knowledge and capabilities in space. Achieving the goals of the proposed R&D in AI/ML-enhanced Fault Management (FM) systems will not only revolutionize NASA's mission capabilities but also find extensive applications in various sectors beyond space exploration. This technology is set to transform any high-value, autonomous system requiring robust fault management to ensure reliability and safety. In the aviation industry, for example, integrating this advanced FM technology could significantly improve the safety and efficiency of both manned and unmanned aircraft, enabling them to autonomously detect and mitigate system failures, thus preventing accidents and reducing maintenance costs. Automotive sectors, especially those developing autonomous vehicles, stand to benefit immensely from this innovation. Enhanced FM systems can provide these vehicles with the ability to autonomously handle unexpected system failures or environmental challenges, increasing passenger safety and reliability. Energy sectors, particularly in the operation of remote or unmanned installations such as offshore platforms or autonomous electrical grids, can use this technology to predict, diagnose, and rectify faults without human intervention, ensuring continuous operation and reducing the risk of catastrophic failures. In addition, this FM technology has potential applications in industrial automation, where machinery and robots could utilize AI/ML-driven FM to autonomously correct operational faults, reducing downtime and maintaining productivity. By transcending the space industry, the Lynntech’s proposed AI/ML FM innovation not only supports NASA's exploration goals but also contributes to enhancing safety, efficiency, and autonomy across a broad spectrum of critical industries, thereby fostering technological advancement and resilience in the face of complex operational challenges.