Application of ML to Detection of Anomalies in Spacecraft Health and Status Data

This effort will use Machine Learning (ML) techniques on Magnetospheric Multiscale (MMS) mission telemetry data to develop a suite of algorithms capable of predicting and detecting mission anomalies. This software will provide new insights in telemetry data patterns and reduce the time it takes to identify anomalies, allowing operators to focus on finding resolutions to ensure spacecraft health and safety. In the future, we may integrate this software with existing Telemetry Tracking and Command (TT&C) systems and NASA technologies to allow for its use in additional spacecraft missions.

This project will benefit current and future mission personnel seeking to streamline detection of anomalies in health and status telemetry data. We will take advantage of readily available mission data and improve upon pre-researched algorithms to provide an extra layer of automation to telemetry processing. This will provide mission engineers with an enhanced anomaly detection capability that can be incorporated in their existing mission operations.

This is early/mid-stage (TRL 4) — 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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