A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies

Reducing satellite failures and keeping satellites healthy in orbit are important issues. Current satellite systems have developed modules to detect anomalies on board. However, they only target a subset of anomaly types and heavily rely on expert knowledge. To address these limitations, this paper...

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Bibliographic Details
Published in:International journal of aerospace engineering 2022-01, Vol.2022, p.1-9
Main Authors: Wang, Yakun, Gong, Jianglei, Zhang, Jie, Han, Xiaodong
Format: Article
Language:English
Subjects:
Aerospace engineering
Algorithms
Anomalies
Datasets
Deep learning
False alarms
Ground stations
Neural networks
Satellites
Telemetry
Variables
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Summary:Reducing satellite failures and keeping satellites healthy in orbit are important issues. Current satellite systems have developed modules to detect anomalies on board. However, they only target a subset of anomaly types and heavily rely on expert knowledge. To address these limitations, this paper proposes a data-driven anomaly detection framework to detect point anomalies. We first propose the Deviation Divide Mean over Neighbors (DDMN) method to figure out the fake anomaly problem caused by data errors in the satellite telemetry data. Then, we use the Long Short-Term Memory (LSTM), a deep learning method, to model the multivariable time-series data, and a Gaussian model to detect anomalies. We applied our approach to the telemetry data collected from sensors on an in-orbit satellite for more than two years and demonstrate its superiority. Moreover, we explored what conditions could lead to false alarms. The approach proposed has been deployed to the ground station to monitor the health status of the in-orbit satellites.
ISSN:1687-5966
1687-5974
DOI:10.1155/2022/1676933