Anomaly detection for data accountability of Mars telemetry data

The Mars Curiosity rover is frequently sending engineering and science data that goes through a pipeline of systems before reaching its final destination at the mission operations center making it prone to volume loss and data corruption. A ground data system analysis (GDSA) team is charged with the monitoring of this flow of information and the detection of anomalies in that data to request a re-transmission when necessary. So far, this task relied primarily on the expertise of GDSA analysts who, especially with new missions launching, require the assistance of an autonomous and effective detection tool. Variational autoencoders are powerful deep neural networks that can learn to isolate such anomalies yet, as any deep network, they require an architectural search and fine-tuning to yield exploitable performance. Furthermore, this process needs to be repeated periodically to adjust to the changing flow of transmissions. This work presents Delta-MADS, a hybrid derivative-free optimization method designed to quickly produce efficient variational autoencoders in order to assist the GDSA team in their mission.

Automated summary

This study introduces Delta-MADS, a hybrid derivative-free optimization method, designed to quickly produce efficient variational autoencoders to assist the GDSA team in their mission.