Spacecraft housekeeping telemetry is monitored at flight control centers by the operations engineers using tools that can perform limit checking or simple trend analysis. Recent developments in machine learning techniques for anomaly detection enables the implementation of more sophisticated systems that aim to augment current state-of-the-art mission tools to provide valuable decision support for the spacecraft operators, assisting in anomaly detection and potentially saving console time for the engineers. We will show some results of the implementation of an anomaly detection tool for the NASA Mars Science Laboratory mission.
Telemetry Anomaly Detection System Using Machine Learning to Streamline Mission Operations
01.09.2017
2435917 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Telemetry packetization for improved mission operations
NTRS | 1976
|Communications streamline yard operations
Engineering Index Backfile | 1954