STGLR: A Spacecraft Anomaly Detection Method Based on Spatio-Temporal Graph Learning
Anomalies frequently occur during the operation of spacecraft in orbit, and studying anomaly detection methods is crucial to ensure the normal operation of spacecraft. Due to the complexity of spacecraft structures, telemetry data possess characteristics such as high dimensionality, complexity, and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/310 |
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| _version_ | 1832587554233253888 |
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| author | Yi Lai Ye Zhu Li Li Qing Lan Yizheng Zuo |
| author_facet | Yi Lai Ye Zhu Li Li Qing Lan Yizheng Zuo |
| author_sort | Yi Lai |
| collection | DOAJ |
| description | Anomalies frequently occur during the operation of spacecraft in orbit, and studying anomaly detection methods is crucial to ensure the normal operation of spacecraft. Due to the complexity of spacecraft structures, telemetry data possess characteristics such as high dimensionality, complexity, and large scale. Existing methods frequently ignore or fail to explicitly extract the correlation between variables, and due to the lack of prior knowledge, it is difficult to obtain the initial relationship of variables. To address these issues, this paper proposes a new method, namely spatio-temporal graph learning reconstruction (STGLR), for spacecraft anomaly detection. STGLR employs a dynamic graph learning module to infer the initial relationships among telemetry variables. It then constructs a spatio-temporal feature extraction module to capture complex spatio-temporal dependencies among variables, leveraging a graph sample and aggregation network to learn embedded features and incorporating an attention mechanism to adaptively select salient features. Finally, a reconstruction module is used to learn the latent representations of features, capturing the normal patterns in telemetry data and achieving anomaly detection. To validate the effectiveness of the proposed method, experiments were conducted on two public spacecraft datasets, and the results demonstrate that the performance of the STGLR method surpasses existing anomaly detection methods, with an average F1 score exceeding 0.97. |
| format | Article |
| id | doaj-art-2c5d962a331a43db8089a9bcc7628c95 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-2c5d962a331a43db8089a9bcc7628c952025-01-24T13:48:27ZengMDPI AGSensors1424-82202025-01-0125231010.3390/s25020310STGLR: A Spacecraft Anomaly Detection Method Based on Spatio-Temporal Graph LearningYi Lai0Ye Zhu1Li Li2Qing Lan3Yizheng Zuo4Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, ChinaInnovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, ChinaInnovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, ChinaInnovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, ChinaInnovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, ChinaAnomalies frequently occur during the operation of spacecraft in orbit, and studying anomaly detection methods is crucial to ensure the normal operation of spacecraft. Due to the complexity of spacecraft structures, telemetry data possess characteristics such as high dimensionality, complexity, and large scale. Existing methods frequently ignore or fail to explicitly extract the correlation between variables, and due to the lack of prior knowledge, it is difficult to obtain the initial relationship of variables. To address these issues, this paper proposes a new method, namely spatio-temporal graph learning reconstruction (STGLR), for spacecraft anomaly detection. STGLR employs a dynamic graph learning module to infer the initial relationships among telemetry variables. It then constructs a spatio-temporal feature extraction module to capture complex spatio-temporal dependencies among variables, leveraging a graph sample and aggregation network to learn embedded features and incorporating an attention mechanism to adaptively select salient features. Finally, a reconstruction module is used to learn the latent representations of features, capturing the normal patterns in telemetry data and achieving anomaly detection. To validate the effectiveness of the proposed method, experiments were conducted on two public spacecraft datasets, and the results demonstrate that the performance of the STGLR method surpasses existing anomaly detection methods, with an average F1 score exceeding 0.97.https://www.mdpi.com/1424-8220/25/2/310anomaly detectionspacecraft telemetry datadynamic graph learningGraphSAGEvariational auto-encoder |
| spellingShingle | Yi Lai Ye Zhu Li Li Qing Lan Yizheng Zuo STGLR: A Spacecraft Anomaly Detection Method Based on Spatio-Temporal Graph Learning Sensors anomaly detection spacecraft telemetry data dynamic graph learning GraphSAGE variational auto-encoder |
| title | STGLR: A Spacecraft Anomaly Detection Method Based on Spatio-Temporal Graph Learning |
| title_full | STGLR: A Spacecraft Anomaly Detection Method Based on Spatio-Temporal Graph Learning |
| title_fullStr | STGLR: A Spacecraft Anomaly Detection Method Based on Spatio-Temporal Graph Learning |
| title_full_unstemmed | STGLR: A Spacecraft Anomaly Detection Method Based on Spatio-Temporal Graph Learning |
| title_short | STGLR: A Spacecraft Anomaly Detection Method Based on Spatio-Temporal Graph Learning |
| title_sort | stglr a spacecraft anomaly detection method based on spatio temporal graph learning |
| topic | anomaly detection spacecraft telemetry data dynamic graph learning GraphSAGE variational auto-encoder |
| url | https://www.mdpi.com/1424-8220/25/2/310 |
| work_keys_str_mv | AT yilai stglraspacecraftanomalydetectionmethodbasedonspatiotemporalgraphlearning AT yezhu stglraspacecraftanomalydetectionmethodbasedonspatiotemporalgraphlearning AT lili stglraspacecraftanomalydetectionmethodbasedonspatiotemporalgraphlearning AT qinglan stglraspacecraftanomalydetectionmethodbasedonspatiotemporalgraphlearning AT yizhengzuo stglraspacecraftanomalydetectionmethodbasedonspatiotemporalgraphlearning |