AstraSense: AI-Driven Health Predictions for Sustainable Aviation

Purpose: The AstraSense system aims to enhance the reliability and safety of hybrid-electric and all-electric aircraft by providing real-time health monitoring and predictive maintenance. It addresses the challenges of operating in high-voltage, high-noise environments by detecting early-stage degradation and predicting failures before they occur. Technology: AstraSense integrates multi-point sensing, harmonic signature analysis, machine learning-based fault prediction, and noise diagnostics. This combination allows for continuous assessment of system integrity and early warnings, enabling autonomous AI or operators to take corrective action before failures become critical. Intended Use of Funding: The requested funding of $150,000 will be used to develop and validate the AI-driven health monitoring architecture, focusing on: AI-based fault detection and diagnostics. Sensor data processing methods. System performance evaluation in simulated high-voltage conditions. Developing a scalable system architecture for health monitoring and predictive maintenance. Aligning the system with NASA's goals for sustainable aviation. Target Markets: The primary target markets include: NASA and aerospace industry: Enhancing safety and reliability for hybrid-electric aircraft. Commercial eVTOL manufacturers and electric aviation developers: Ensuring safer and more reliable electric propulsion systems. Defense and industrial sectors: Reducing downtime and maintenance costs through predictive maintenance. This innovation supports the transition to sustainable aviation by improving fault detection, system integration, and predictive maintenance, ultimately enhancing flight safety and operational efficiency.