Anomaly Detection in Satellite Telemetry Data Using a Sparse Feature-Based Method
Anomaly detection based on telemetry data is a major issue in satellite health monitoring which can identify unusual or unexpected events, helping to avoid serious accidents and ensure the safety and reliability of operations. In recent years, sparse representation techniques have received an increa...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/17/6358 |
| _version_ | 1797493240594694144 |
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| author | Jiahui He Zhijun Cheng Bo Guo |
| author_facet | Jiahui He Zhijun Cheng Bo Guo |
| author_sort | Jiahui He |
| collection | DOAJ |
| description | Anomaly detection based on telemetry data is a major issue in satellite health monitoring which can identify unusual or unexpected events, helping to avoid serious accidents and ensure the safety and reliability of operations. In recent years, sparse representation techniques have received an increasing amount of interest in anomaly detection, although its applications in satellites are still being explored. In this paper, a novel sparse feature-based anomaly detection method (SFAD) is proposed to identify hybrid anomalies in telemetry. First, a telemetry data dictionary and the corresponding sparse matrix are obtained through K-means Singular Value Decomposition (K-SVD) algorithms, then sparse features are defined from the sparse matrix containing the local dynamics and co-occurrence relations in the multivariate telemetry time series. Finally, lower-dimensional sparse features vectors are input to a one-class support vector machine (OCSVM) to detect anomalies in telemetry. Case analysis based on satellite antenna telemetry data shows that the detection precision, F1-score and FPR of the proposed method are improved compared with other existing multivariate anomaly detection methods, illustrating the good effectiveness of this method. |
| first_indexed | 2024-03-10T01:17:11Z |
| format | Article |
| id | doaj.art-7f5c0f70d3564121afed4b8e397228fe |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:17:11Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7f5c0f70d3564121afed4b8e397228fe2023-11-23T14:07:07ZengMDPI AGSensors1424-82202022-08-012217635810.3390/s22176358Anomaly Detection in Satellite Telemetry Data Using a Sparse Feature-Based MethodJiahui He0Zhijun Cheng1Bo Guo2College of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaAnomaly detection based on telemetry data is a major issue in satellite health monitoring which can identify unusual or unexpected events, helping to avoid serious accidents and ensure the safety and reliability of operations. In recent years, sparse representation techniques have received an increasing amount of interest in anomaly detection, although its applications in satellites are still being explored. In this paper, a novel sparse feature-based anomaly detection method (SFAD) is proposed to identify hybrid anomalies in telemetry. First, a telemetry data dictionary and the corresponding sparse matrix are obtained through K-means Singular Value Decomposition (K-SVD) algorithms, then sparse features are defined from the sparse matrix containing the local dynamics and co-occurrence relations in the multivariate telemetry time series. Finally, lower-dimensional sparse features vectors are input to a one-class support vector machine (OCSVM) to detect anomalies in telemetry. Case analysis based on satellite antenna telemetry data shows that the detection precision, F1-score and FPR of the proposed method are improved compared with other existing multivariate anomaly detection methods, illustrating the good effectiveness of this method.https://www.mdpi.com/1424-8220/22/17/6358anomaly detectiontelemetry datasparse featuresOCSVMtime series |
| spellingShingle | Jiahui He Zhijun Cheng Bo Guo Anomaly Detection in Satellite Telemetry Data Using a Sparse Feature-Based Method Sensors anomaly detection telemetry data sparse features OCSVM time series |
| title | Anomaly Detection in Satellite Telemetry Data Using a Sparse Feature-Based Method |
| title_full | Anomaly Detection in Satellite Telemetry Data Using a Sparse Feature-Based Method |
| title_fullStr | Anomaly Detection in Satellite Telemetry Data Using a Sparse Feature-Based Method |
| title_full_unstemmed | Anomaly Detection in Satellite Telemetry Data Using a Sparse Feature-Based Method |
| title_short | Anomaly Detection in Satellite Telemetry Data Using a Sparse Feature-Based Method |
| title_sort | anomaly detection in satellite telemetry data using a sparse feature based method |
| topic | anomaly detection telemetry data sparse features OCSVM time series |
| url | https://www.mdpi.com/1424-8220/22/17/6358 |
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