{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:07:43Z","timestamp":1787029663008,"version":"build-2736575974"},"reference-count":41,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Excellent Fund of Youth Innovation Promotion Association of Chinese Academy of Sciences","award":["Y2022078"],"award-info":[{"award-number":["Y2022078"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s25020310","type":"journal-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T05:06:34Z","timestamp":1736226394000},"page":"310","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["STGLR: A Spacecraft Anomaly Detection Method Based on Spatio-Temporal Graph Learning"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9393-7201","authenticated-orcid":false,"given":"Yi","family":"Lai","sequence":"first","affiliation":[{"name":"Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101408, China"},{"name":"Key Lab for Satellite Digitalization Technology of Chinese Academy of Sciences, Shanghai 200031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Zhu","sequence":"additional","affiliation":[{"name":"Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101408, China"},{"name":"Key Lab for Satellite Digitalization Technology of Chinese Academy of Sciences, Shanghai 200031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7588-7238","authenticated-orcid":false,"given":"Li","family":"Li","sequence":"additional","affiliation":[{"name":"Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101408, China"},{"name":"Key Lab for Satellite Digitalization Technology of Chinese Academy of Sciences, Shanghai 200031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3693-9202","authenticated-orcid":false,"given":"Qing","family":"Lan","sequence":"additional","affiliation":[{"name":"Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101408, China"},{"name":"Key Lab for Satellite Digitalization Technology of Chinese Academy of Sciences, Shanghai 200031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3551-957X","authenticated-orcid":false,"given":"Yizheng","family":"Zuo","sequence":"additional","affiliation":[{"name":"Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101408, China"},{"name":"Key Lab for Satellite Digitalization Technology of Chinese Academy of Sciences, Shanghai 200031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3201","DOI":"10.1007\/s10462-019-09760-1","article-title":"Machine learning in telemetry data mining of space mission: Basics, challenging and future directions","volume":"53","author":"Hassanien","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_2","unstructured":"Yairi, T., Nakatsugawa, M., Hori, K., Nakasuka, S., Machida, K., and Ishihama, N. (2004, January 10\u201313). Adaptive limit checking for spacecraft telemetry data using regression tree learning. Proceedings of the 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583), The Hague, The Netherlands."},{"key":"ref_3","unstructured":"Lee, R.K., Watson, R., Kitts, C., Stang, P., and Palmintier, B. (2004, January 6\u201313). Anomaly detection using the emerald nanosatellite on board expert system. Proceedings of the 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No. 04TH8720), Big Sky, MT, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"109929","DOI":"10.1016\/j.measurement.2021.109929","article-title":"Detection and analysis of real-time anomalies in large-scale complex system","volume":"184","author":"Chen","year":"2021","journal-title":"Measurement"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2226","DOI":"10.1109\/TII.2013.2243743","article-title":"From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis","volume":"9","author":"Dai","year":"2013","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mehrmolaei, S., and Keyvanpour, M.R. (2016, January 9). Time series forecasting using improved ARIMA. Proceedings of the 2016 Artificial Intelligence and Robotics (IRANOPEN), Qazvin, Iran.","DOI":"10.1109\/RIOS.2016.7529496"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.trc.2016.01.007","article-title":"Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring","volume":"64","author":"Li","year":"2016","journal-title":"Transp. Res. Part Emerg. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"451","DOI":"10.32614\/RJ-2016-058","article-title":"Distance Measures for Time Series in R: The TSdist Package","volume":"8","author":"Mori","year":"2016","journal-title":"R J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106919","DOI":"10.1016\/j.asoc.2020.106919","article-title":"Clustering-based anomaly detection in multivariate time series data","volume":"100","author":"Li","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.asoc.2016.11.041","article-title":"Measures in the time and frequency domains for fitness landscape analysis of dynamic optimization problems","volume":"51","author":"Lu","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_11","unstructured":"Filonov, P., Lavrentyev, A., and Vorontsov, A. (2016). Multivariate industrial time series with cyber-attack simulation: Fault detection using an lstm-based predictive data model. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hundman, K., Constantinou, V., Laporte, C., Colwell, I., and Soderstrom, T. (2018, January 19\u201323). Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.","DOI":"10.1145\/3219819.3219845"},{"key":"ref_13","first-page":"161","article-title":"Vector autoregressive model-based anomaly detection in aviation systems","volume":"13","author":"Melnyk","year":"2016","journal-title":"J. Aerosp. Inf. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/S0925-2312(01)00706-8","article-title":"Recurrent neural networks for time series classification","volume":"50","author":"Stagge","year":"2003","journal-title":"Neurocomputing"},{"key":"ref_15","unstructured":"Xu, J., Wu, H., Wang, J., and Long, M. (2021). Anomaly transformer: Time series anomaly detection with association discrepancy. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., and Pei, D. (2019, January 4\u20138). Robust anomaly detection for multivariate time series through stochastic recurrent neural network. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330672"},{"key":"ref_17","first-page":"1","article-title":"Sparse autoencoder","volume":"72","author":"Ng","year":"2011","journal-title":"CS294A Lect. Notes"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.neucom.2021.12.093","article-title":"GAN-based anomaly detection: A review","volume":"493","author":"Xia","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3891","DOI":"10.1109\/TII.2023.3314852","article-title":"MAG: A novel approach for effective anomaly detection in spacecraft telemetry data","volume":"20","author":"Yu","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Guan, S., Zhao, B., Dong, Z., Gao, M., and He, Z. (2022). GTAD: Graph and temporal neural network for multivariate time series anomaly detection. Entropy, 24.","DOI":"10.3390\/e24060759"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s40649-019-0069-y","article-title":"Graph convolutional networks: A comprehensive review","volume":"6","author":"Zhang","year":"2019","journal-title":"Comput. Soc. Netw."},{"key":"ref_22","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017). Graph attention networks. arXiv."},{"key":"ref_23","unstructured":"Hamilton, W., Ying, Z., and Leskovec, J. (2017). Inductive representation learning on large graphs. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1007\/JHEP08(2021)080","article-title":"Anomaly detection with convolutional graph neural networks","volume":"2021","author":"Atkinson","year":"2021","journal-title":"J. High Energy Phys."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhao, H., Wang, Y., Duan, J., Huang, C., Cao, D., Tong, Y., Xu, B., Bai, J., Tong, J., and Zhang, Q. (2020, January 17\u201320). Multivariate time-series anomaly detection via graph attention network. Proceedings of the 2020 IEEE International Conference on Data Mining (ICDM), Sorrento, Italy.","DOI":"10.1109\/ICDM50108.2020.00093"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Deng, A., and Hooi, B. (2021, January 2\u20139). Graph neural network-based anomaly detection in multivariate time series. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual.","DOI":"10.1609\/aaai.v35i5.16523"},{"key":"ref_27","unstructured":"Shang, C., Chen, J., and Bi, J. (2021). Discrete graph structure learning for forecasting multiple time series. arXiv."},{"key":"ref_28","unstructured":"Chen, K., Feng, M., and Wirjanto, T.S. (2023). Multivariate time series anomaly detection via dynamic graph forecasting. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, W., Zhang, C., and Tsung, F. (2022, January 23\u201329). GRELEN: Multivariate Time Series Anomaly Detection from the Perspective of Graph Relational Learning. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22), Vienna, Austria.","DOI":"10.24963\/ijcai.2022\/332"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"109423","DOI":"10.1016\/j.patcog.2023.109423","article-title":"Dynamic graph structure learning for multivariate time series forecasting","volume":"138","author":"Li","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cho, K. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5388","DOI":"10.1109\/TKDE.2023.3333824","article-title":"Spatio-temporal graph neural networks for predictive learning in urban computing: A survey","volume":"36","author":"Jin","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, X., Cao, R., Zhang, Z., and Xia, Y. (2020, January 19\u201324). Crowd flow forecasting with multi-graph neural networks. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9207457"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hochreiter, S. (1997). Long Short-Term Memory, Neural Computation MIT-Press.","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref_35","unstructured":"Vaswani, A. (2017, January 4\u20139). Attention is all you need. Advances in Neural Information Processing Systems. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_36","unstructured":"Kingma, D.P. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1109\/LRA.2018.2801475","article-title":"A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder","volume":"3","author":"Park","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, D., Chen, D., Jin, B., Shi, L., Goh, J., and Ng, S.K. (2019, January 17\u201319). MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. Proceedings of the 28th International Conference on Artificial Neural Networks, Munich, Germany.","DOI":"10.1007\/978-3-030-30490-4_56"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhao, Y., Han, J., Su, Y., Jiao, R., Wen, X., and Pei, D. (2021, January 14\u201318). Multivariate time series anomaly detection and interpretation using hierarchical inter-metric and temporal embedding. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Singapore.","DOI":"10.1145\/3447548.3467075"},{"key":"ref_40","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_41","unstructured":"Defferrard, M., Bresson, X., and Vandergheynst, P. (2016). Convolutional neural networks on graphs with fast localized spectral filtering. Adv. Neural Inf. Process. Syst., 29."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/2\/310\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:24:17Z","timestamp":1759919057000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/2\/310"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,7]]},"references-count":41,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["s25020310"],"URL":"https:\/\/doi.org\/10.3390\/s25020310","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,7]]}}}