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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/1676933 |
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| _version_ | 1832565215104860160 |
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| author | Yakun Wang Jianglei Gong Jie Zhang Xiaodong Han |
| author_facet | Yakun Wang Jianglei Gong Jie Zhang Xiaodong Han |
| author_sort | Yakun Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-16db5908eb4d4a078ce466217eae9012 |
| institution | Kabale University |
| issn | 1687-5974 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-16db5908eb4d4a078ce466217eae90122025-02-03T01:08:58ZengWileyInternational Journal of Aerospace Engineering1687-59742022-01-01202210.1155/2022/1676933A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake AnomaliesYakun Wang0Jianglei Gong1Jie Zhang2Xiaodong Han3Institute of Telecommunication and Navigation SatellitesInstitute of Telecommunication and Navigation SatellitesInstitute of Telecommunication and Navigation SatellitesInstitute of Telecommunication and Navigation SatellitesReducing 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.http://dx.doi.org/10.1155/2022/1676933 |
| spellingShingle | Yakun Wang Jianglei Gong Jie Zhang Xiaodong Han A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies International Journal of Aerospace Engineering |
| title | A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies |
| title_full | A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies |
| title_fullStr | A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies |
| title_full_unstemmed | A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies |
| title_short | A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies |
| title_sort | deep learning anomaly detection framework for satellite telemetry with fake anomalies |
| url | http://dx.doi.org/10.1155/2022/1676933 |
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