Towards Adaptive and Robust Unsupervised Anomaly Detection in Satellite Telemetry
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Published
Jan 13, 2026
Lorenzo Brancato
Alessandro Lucchetti
Francesco Cadini
Marco Giglio
Abstract
The role of satellites and space systems in space exploration is of paramount importance, particularly given the growing interest in this field. The considerable financial outlay and commitment of resources that are necessary for space missions, coupled with the inherent difficulty of directly intervening in the systems in question in the event of faults or anomalies, give rise to the imperative of designing and developing highly reliable platforms. However, the harsh environment in which these systems operate means that unexpected and undesired events may occur, with the potential to have a catastrophic impact on mission objectives. To address this issue, it is of paramount importance to use telemetry data for the prompt detection of anomalies. This facilitates the implementation of corrective or mitigating actions, although this is challenging due to the remoteness of space platforms and the limited range of intervention options. A proactive approach can extend the duration of the mission, thereby preventing the loss of data and functions that are scientifically or commercially valuable. However, the intricate and multifaceted nature of telemetry data, which encompasses both sensor readings and commands, poses considerable analytical challenges, reinforcing the ongoing necessity of experts to determine system integrity.
Most existing anomaly detection algorithms depend excessively on extensive hyperparameter tuning and fixed thresholds, which renders them less robust when applied to different signals or evolving scenarios. Furthermore, numerous algorithms necessitate voluminous amounts of labeled data and protracted training phases, which results in increased deployment latency and operational costs, thereby limiting their utility throughout the duration of a mission.
To address these limitations, a novel framework for intelligent time-series anomaly detection (TSAD) is proposed. The solution has been designed to be plug-and-play, thereby minimizing the necessity of hyperparameter tuning and ensuring robust performance across diverse scenarios without the need for optimization. The system incorporates an adaptive thresholding mechanism that automatically adapts to evolving time-series behavior, ultimately enhancing detection accuracy. Furthermore, it requires only a statistically significant volume of unlabeled data, drastically reducing deployment latency and enabling rapid operational integration.
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Keywords
Satellite Telemetry, Time-Series Anomaly Detection, Unsupervised Learning, Adaptive Thresholding
References
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Unmasking overestimation: a re-evaluation of deep anomaly detection in spacecraft telemetry. CEAS Space Journal, 16(2), 225–237. Hu, X., Xu, L., Lin, X., & Pecht, M. (2020). Battery lifetime prognostics. Joule, 4(2), 310–346.
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Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning. Signal Processing, 168, 107320. Ruszczak, B., Kotowski, K., Andrzejewski, J., Musiał, A., Evans, D., Zelenevskiy, V., . . . Nalepa, J. (2023). Machine learning detects anomalies in ops-sat telemetry. In International conference on computational science (pp. 295–306).
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Schmidl, S., Wenig, P., & Papenbrock, T. (2022). Anomaly detection in time series: a comprehensive evaluation. Proceedings of the VLDB Endowment, 15(9), 1779– 1797.
Shimillas, C., Malialis, K., Fokianos, K., & Polycarpou, M. M. (2025). Transformer-based multivariate time series anomaly localization. arXiv preprint arXiv:2501.08628. Retrieved from https://arxiv.org/abs/2501.08628 doi: 10.48550/arXiv.2501.08628
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Yairi, T., Takeishi, N., Oda, T., Nakajima, Y., Nishimura, N., & Takata, N. (2017). A data-driven health monitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction. IEEE Transactions on Aerospace and Electronic Systems, 53(3), 1384–1401.
Zamanzadeh Darban, Z., Webb, G. I., Pan, S., Aggarwal, C., & Salehi, M. (2024). Deep learning for time series anomaly detection: A survey. ACM Computing Surveys, 57(6), 1–42. Retrieved from https://dl.acm.org/doi/10.1145/3691338 doi: 10.1145/3691338
Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W., . . . Chawla, N. V. (2019). A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. In Proceedings of the aaai conference on artificial intelligence (Vol. 33, pp. 1409–1416).
Zhang, Z., Tavenard, R., Bailly, A., Tang, X., Tang, P., & Corpetti, T. (2017). Dynamic time warping under limited warping path length. Information Sciences, 393, 91–107.
Zhong, S., & Mueen, A. (2024). Mass: distance profile of a query over a time series. Data Mining and Knowledge Discovery, 38(3), 1466–1492.
Correia, L., Goos, J.-C., Klein, P., B¨ack, T., & Kononova, A. V. (2024). Online model-based anomaly detection in multivariate time series: Taxonomy, survey, research challenges and future directions. Engineering Applications of Artificial Intelligence, 133, 109323. Retrieved from https://doi.org/10.1016/j.engappai.2024.109323 doi: 10.1016/j.engappai.2024.109323
El Amine Sehili, M., & Zhang, Z. (2023). Multivariate time series anomaly detection: Fancy algorithms and flawed evaluation methodology. In Technology conference on performance evaluation and benchmarking (pp. 1–17).
Evans, D., Martinez, J., Korte-Stapff, M., Brighenti, A., Brighenti, C., & Biancat, J. (2017). Data mining to drastically improve spacecraft telemetry checking. In Space operations: Contributions from the global community (pp. 87–113). Springer.
Fuertes, S., Picart, G., Tourneret, J.-Y., Chaari, L., Ferrari, A., & Richard, C. (2016). Improving spacecraft health monitoring with automatic anomaly detection techniques. In 14th international conference on space operations (p. 2430).
Fuertes, S., Pilastre, B., & D’Escrivan, S. (2018). Performance assessment of nostradamus & other machine learning-based telemetry monitoring systems on a spacecraft anomalies database. In 2018 spaceops conference (p. 2559). Herrmann, L., Bieber, M., Verhagen, W. J., Cosson, F., & Santos, B. F. (2024).
Unmasking overestimation: a re-evaluation of deep anomaly detection in spacecraft telemetry. CEAS Space Journal, 16(2), 225–237. Hu, X., Xu, L., Lin, X., & Pecht, M. (2020). Battery lifetime prognostics. Joule, 4(2), 310–346.
Hundman, K., Constantinou, V., Laporte, C., Colwell, I., & Soderstrom, T. (2018). Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding. In Proceedings of the 24th acm sigkdd international conference on knowledge discovery & data mining (pp. 387–395).
Iqbal, A., & Amin, R. (2024). Time series forecasting and anomaly detection using deep learning. Computers & Chemical Engineering, 182, 108560. Retrieved from https://www.sciencedirect.com/science/article/pii/S0098135423004301 doi: 10.1016/j.compchemeng.2023.108560
Kang, H., & Kang, P. (2024). Transformer-based multivariate time series anomaly detection using inter-variable attention mechanism. Knowledge- Based Systems, 290, 111507. Retrieved from https://doi.org/10.1016/j.knosys.2024.111507 doi: 10.1016/j.knosys.2024.111507
Kim, J., Kang, H., & Kang, P. (2023). Time-series anomaly detection with stacked transformer representations and 1d convolutional network. Engineering Applications of Artificial Intelligence, 120, 105964. Retrieved from https://doi.org/10.1016/j.engappai.2023.105964 doi: 10.1016/j.engappai.2023.105964
Kim, J., Park, S., Lee, M., & Cho, S. (2025). A comprehensive survey of deep learning for time series: Architectures, challenges, and emerging directions. Artificial Intelligence Review. Retrieved from https://doi.org/10.1007/s10462-025-11223-9 (Online first 2025) doi: 10.1007/s10462-025-11223-9
Koochali, A., Bauer, D., Giglio, A., Kugler, S., & G¨orner, L. (2025). Vaeneu: A new avenue for VAE application on probabilistic forecasting. Applied Intelligence. Retrieved from https://doi.org/10.1007/s10489-024-06203-5 (Online first 2025) doi: 10.1007/s10489-024-06203-5
Kotowski, K., Haskamp, C., Andrzejewski, J., Ruszczak, B., Nalepa, J., Lakey, D., . . . Martinez-Heras, J. (2024). European space agency benchmark for anomaly detection in satellite telemetry. arXiv preprint arXiv:2406.17826. KP Labs. (2024).
Esa anomaly detection benchmark. https://github.com/kplabs-pl/ESA-ADB. (GitHub repository; accessed 2025-07-16) Lakey, D., & Schlippe, T. (2024). A comparison of deep learning architectures for spacecraft anomaly detection. In 2024 ieee aerospace conference (pp. 1–11).
Lee, J.,Wu, F., Zhao,W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—reviews, methodology and applications. Mechanical systems and signal processing, 42(1-2), 314–334.
Leushuis, R. M., Matthijsse, P., Ophelders, T., Aarts, E., & Pechenizkiy, M. (2025). Probabilistic forecasting with var-vae: Advancing time series forecasting with vector autoregression in the latent space. Information Sciences, 692, 122184. Retrieved from https://doi.org/10.1016/j.ins.2025.122184 doi: 10.1016/j.ins.2025.122184
Lutz, R. R., & Mikulski, I. C. (2004). Empirical analysis of safety-critical anomalies during operations. IEEE Transactions on Software Engineering, 30(3), 172– 180.
Martinez, J. (2012). Drmust-a data mining approach for anomaly investigation. In Spaceops 2012 (p. 1275109).
Martinez, J., & Donati, A. (2018). Novelty detection with deep learning. In 2018 spaceops conference (p. 2560).Neloy, A. A., & Turgeon, M. (2024). A comprehensive study of auto-encoders for anomaly detection: Efficiency and trade-offs. Machine Learning with Applications, 17, 100572. Retrieved from https://www.sciencedirect.com/science/article/pii/S2666827024000483 doi: 10.1016/j.mlwa.2024.100572
OMeara, C., Schlag, L., Faltenbacher, L., & Wickler, M. (2016). Athmos: automated telemetry health monitoring system at gsoc using outlier detection and supervised machine learning. In 14th international conference on space operations (p. 2347). Pilastre, B., Boussouf, L., d’Escrivan, S., & Tourneret, J.- Y. (2020).
Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning. Signal Processing, 168, 107320. Ruszczak, B., Kotowski, K., Andrzejewski, J., Musiał, A., Evans, D., Zelenevskiy, V., . . . Nalepa, J. (2023). Machine learning detects anomalies in ops-sat telemetry. In International conference on computational science (pp. 295–306).
Ruszczak, B., Kotowski, K., Evans, D., & Nalepa, J. (2025). The ops-sat benchmark for detecting anomalies in satellite telemetry. Scientific Data, 12(1), 710. Sakoe, H., & Chiba, S. (2003). Dynamic programming algorithm optimization for spoken word recognition. IEEE transactions on acoustics, speech, and signal processing, 26(1), 43–49.
Schmidl, S., Wenig, P., & Papenbrock, T. (2022). Anomaly detection in time series: a comprehensive evaluation. Proceedings of the VLDB Endowment, 15(9), 1779– 1797.
Shimillas, C., Malialis, K., Fokianos, K., & Polycarpou, M. M. (2025). Transformer-based multivariate time series anomaly localization. arXiv preprint arXiv:2501.08628. Retrieved from https://arxiv.org/abs/2501.08628 doi: 10.48550/arXiv.2501.08628
Silva, D. F., Yeh, C.-C. M., Batista, G. E., & Keogh, E. J. (2016). Simple: Assessing music similarity using subsequences joins. In Ismir (pp. 23–29). Stefan, A., Athitsos, V., & Das, G. (2012). The move-splitmerge metric for time series. IEEE transactions on Knowledge and Data Engineering, 25(6), 1425–1438.
Tariq, S., Lee, S., Shin, Y., Lee, M. S., Jung, O., Chung, D., & Woo, S. S. (2019). Detecting anomalies in space using multivariate convolutional lstm with mixtures of probabilistic pca. In Proceedings of the 25th acm sigkdd international conference on knowledge discovery & data mining (pp. 2123–2133).
Tsutsumi, S., Hirabayashi, M., Sato, D., Kawatsu, K., Sato, M., Kimura, T., . . . Abe, M. (2021). Data-driven fault detection in a reusable rocket engine using bivariate time-series analysis. Acta Astronautica, 179, 685–694.
Vlachos, M., Hadjieleftheriou, M., Gunopulos, D., & Keogh, E. (2003). Indexing multi-dimensional time-series with support for multiple distance measures. In Proceedings of the ninth acm sigkdd international conference on knowledge discovery and data mining (pp. 216–225).
Vlachos, M., Kollios, G., & Gunopulos, D. (2002). Discovering similar multidimensional trajectories. In Proceedings 18th international conference on data engineering (pp. 673–684).
Wagner, D., Michels, T., Schulz, F. C., Nair, A., Rudolph, M., & Kloft, M. (2023). Timesead: Benchmarking deep multivariate time-series anomaly detection. Transactions on Machine Learning Research.
Wang, F., Jiang, Y., Zhang, R., Wei, A., Xie, J., & Pang, X. (2025). A survey of deep anomaly detection in multivariate time series: Taxonomy, applications, and directions. Sensors, 25(1), 190. Retrieved from https://www.mdpi.com/1424-8220/25/1/190 doi: 10.3390/s25010190
Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., & Sun, L. (2023). Transformers in time series: A survey. In Proceedings of the 32nd international joint conference on artificial intelligence (ijcai-23) (pp. 5605–5612). IJCAI. Retrieved from https://www.ijcai.org/proceedings/2023/0759.pdf doi: 10.24963/ijcai.2023/759
Wu, J. (2005). Liquid-propellant rocket engines healthmonitoring— a survey. Acta Astronautica, 56(3), 347– 356. Wu, R., & Keogh, E. J. (2021). Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress. IEEE transactions on knowledge and data engineering, 35(3), 2421–2429.
Wu, X., Qiu, X., Gao, H., Hu, J., Yang, B., & Guo, C. (2025). k2vae: A koopman-kalman enhanced variational autoencoder for probabilistic time series forecasting. arXiv preprint arXiv:2505.23017. Retrieved from https://arxiv.org/abs/2505.23017 (Also presented at ICML 2025)
Yairi, T., Takeishi, N., Oda, T., Nakajima, Y., Nishimura, N., & Takata, N. (2017). A data-driven health monitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction. IEEE Transactions on Aerospace and Electronic Systems, 53(3), 1384–1401.
Zamanzadeh Darban, Z., Webb, G. I., Pan, S., Aggarwal, C., & Salehi, M. (2024). Deep learning for time series anomaly detection: A survey. ACM Computing Surveys, 57(6), 1–42. Retrieved from https://dl.acm.org/doi/10.1145/3691338 doi: 10.1145/3691338
Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W., . . . Chawla, N. V. (2019). A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. In Proceedings of the aaai conference on artificial intelligence (Vol. 33, pp. 1409–1416).
Zhang, Z., Tavenard, R., Bailly, A., Tang, X., Tang, P., & Corpetti, T. (2017). Dynamic time warping under limited warping path length. Information Sciences, 393, 91–107.
Zhong, S., & Mueen, A. (2024). Mass: distance profile of a query over a time series. Data Mining and Knowledge Discovery, 38(3), 1466–1492.
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Regular Session Papers
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