Sustainable Aviation Prognostics and Predictive Health Integrated Reliability Engine

As NASA pursues net-zero aviation emissions by 2050, new hybrid-electric, all-electric, and hydrogen aircraft present unique health management challenges. These novel propulsion systems require innovative approaches to detect anomalies in complex integrated architectures. Cybernet proposes SAPPHIRE (Sustainable Aviation Prognostics and Predictive Health Integrated Reliability Engine) to address these needs. SAPPHIRE provides health management specifically for sustainable aviation platforms. Building on Cybernet's predictive maintenance experience with military vehicles, it combines data collection with AI analytics to monitor electric and alternative fuel propulsion components. The system interfaces with aircraft data buses (MIL-STD-1553, ARINC-429, Ethernet) to process high-frequency measurements from electric propulsion systems. SAPPHIRE employs AI algorithms combining dimensionality reduction (PCA, ICA) with deep learning (LSTM networks) to detect degradation in motors, power converters, and hydrogen components. Context-aware analysis distinguishes normal operational variations from genuine anomalies by incorporating mission profiles and environmental conditions. The maintenance support framework translates detected anomalies into actionable recommendations with remaining useful life estimates. Phase I will develop the core architecture, initial AI models, and demonstrate feasibility through simulation. This foundation enables Phase II development of a prototype for integration with sustainable aircraft platforms, progressing from TRL 3 to TRL 6. By enabling early detection of issues and condition-based maintenance, SAPPHIRE enhances safety, reduces costs, and improves efficiency for sustainable aviation vehicles. Its open architecture ensures adaptability across platforms, supporting NASA's transition to environmentally friendly aircraft with the reliability needed for commercial adoption