Edge Machine Learning Predictive Anomaly Detection for Autonomous Operations

Utilize tinyML techniques to implement ML at the edge. Integrate tinyML models with the NASA Platform for Autonomous Systems (NPAS) to enhance fault detection and prediction of future anomalies. Demonstrate the ability of tinyML-enhanced NPAS to autonomously detect cavitation in cryogenic pumps and take appropriate action to safeguard the system.

Benefits include providing advanced tools to operate and optimize critical ground facilities performance. Intelligent autonomous systems have the capacity to bolster these systems by integrating machine learning (ML) with integrated system health management (ISHM) and facilitate improved decision making, potentially with little to no need for human intervention.

This is early/mid-stage (TRL 5) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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