|Distributed and Stacked Neural Network for Anomaly Detection in Small Satellites
The International Space Station (ISS) plans to launch 100+ small-sat missions for different science experiments in the next coming years. At present these missions are limited to couple of months but in the future these will last longer and it becomes crucial to monitor and predict future health of these systems as they age to prolong the usage time. This paper describes a hierarchical architecture which combines data-driven anomaly detection methods with a fine-grained model based diagnosis and prognostics architecture.
|Year of Publication
15th Annual Cubesat Developer Workshop
San Luis Obispo
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