A Comparative Analysis of Anomaly Detection Techniques for Battery Telemetry Data in Low Earth Orbit Remote Sensing Satellites
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Published
Sep 4, 2025
Ahmed Adam
Ahmed Mokhtar
Ahmed Mattar
Mohamed Abdrahman
Sherif Helmy
Abstract
The article presents a comprehensive assessment of telemetry data of batteries used in low-earth orbit satellites. The study further performs an analysis of the performance of using different anomaly detection techniques, including Statistical (Z-Score), Machine Learning (One class support vector machine OCSVM, Isolation Forest), Deep Learning (Autoencoder), and Hybrid Approaches (Autoencoder and neural network and Autoencoder and Z-score). This study introduces and evaluates a hybrid anomaly detection framework combining deep learning-based feature compression (Autoencoder) with various downstream classifiers. The models are validated on real satellite telemetry data and benchmarked using medical electrocardiogram ECG datasets for generalizability. In addition, the study continues to analyze the system by detecting the faulty sensor that was responsible for the detected anomalies, which can help the operators to get a more accurate analysis of the system.
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Keywords
Satellite Battery Telemetry, Anomaly Detection, Z-score, OCSVM, Isolation forest, Autoencoder, Hybrid approaches, Fault Detection
References
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Chikodili, N. B., Abdulmalik, M. D., Abisoye, O. A., & Bashir, S. A. (2020). Outlier detection in multivariate time series data using a fusion of k-medoid, standardized euclidean distance and z-score. In International conference on information and communication technology and applications (pp. 259–271).
Chliah, H., Battou, A., Laoufi, A., et al. (2023). Hybrid machine learning-based approach for anomaly detection using apache spark. International Journal of Advanced Computer Science and Applications, 14(4).
Dhamodharan, B. (2022). Beyond traditional methods: A novel approach to anomaly detection and classification using ai techniques. Transactions on Latest Trends in Artificial Intelligence, 3(3).
Dobos, D., Nguyen, T. T., Dang, T., Wilson, A., Corbett, H., McCall, J., & Stockton, P. (2023). A comparative study of anomaly detection methods for gross error detection problems. Computers & Chemical Engineering, 175, 108263.
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Erfani, S. M., Rajasegarar, S., Karunasekera, S., & Leckie, C. (2016). High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning. Pattern Recognition, 58, 121–134.
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Gao, T., & Lu,W. (2021). Machine learning toward advanced energy storage devices and systems. Iscience, 24(1).
Gonzalez-Jimenez, D., del Olmo, J., Poza, J., Garramiola, F., & Sarasola, I. (2021). Machine learning-based fault detection and diagnosis of faulty power connections of induction machines. Energies, 14(16), 4886.
Grabaskas, N., & Si, D. (2017). Anomaly detection from kepler satellite time-series data. In Machine learning and data mining in pattern recognition: 13th international conference, mldm 2017, new york, ny, usa, july 15-20, 2017, proceedings 13 (pp. 220–232).
He, J., Cheng, Z., & Guo, B. (2022). Anomaly detection in satellite telemetry data using a sparse feature-based method. Sensors, 22(17), 6358.
Huc, A., Salej, J., & Trebar, M. (2021). Analysis of machine learning algorithms for anomaly detection on edge devices. Sensors, 21(14), 4946.
Iqbal, R., Maniak, T., Doctor, F., & Karyotis, C. (2019). Fault detection and isolation in industrial processes using deep learning approaches. IEEE Transactions on Industrial Informatics, 15, 3077–3084.
Irsoy, O., & Alpaydın, E. (2017). Unsupervised feature extraction with autoencoder trees. Neurocomputing, 258, 63–73.
Jan, S. U., Lee, Y. D., & Koo, I. S. (2021). A distributed sensor-fault detection and diagnosis framework using machine learning. Information Sciences, 547, 777–796.
Jeong, S.-K., Ji, D.-H., Oh, M., Park, H., Baeg, S., & Lee, J. (2023). A study on anomaly detection of unmanned marine systems using machine learning. Measurement and Control, 56(3-4), 470–480.
Jung, S., Kim, M., Kim, E., Kim, B., Kim, J., Cho, K.-H., . . . Kim, S. (2024). The early detection of faults for lithium-ion batteries in energy storage systems using independent component analysis with mahalanobis distance. Energies, 17(2), 535.
Kalinichenko, L., Shanin, I., & Taraban, I. (2014). Methods for anomaly detection: A survey. In Ceur workshop proceedings (Vol. 1297, p. 2025).
Kang, H., & Kang, P. (2024). Transformer-based multivariate time series anomaly detection using inter-variable attention mechanism. Knowledge-Based Systems, 290, 111507.
Kea, K., Han, Y., & Kim, T.-K. (2023). Enhancing anomaly detection in distributed power systems using autoencoder-based federated learning. Plos one, 18(8), e0290337.
Kim, G. G., Lee,W., Bhang, B. G., Choi, J. H., & Ahn, H.-K. (2020). Fault detection for photovoltaic systems using multivariate analysis with electrical and environmental variables. IEEE Journal of photovoltaics, 11(1), 202–212.
Kunang, Y. N., Nurmaini, S., Stiawan, D., Zarkasi, A., et al. (2018). Automatic features extraction using autoencoder in intrusion detection system. In 2018 international conference on electrical engineering and computer science (icecos) (pp. 219–224).
Lee, B., & Wang, X. (2010). Fault detection and reconstruction for micro-satellite power subsystem based on pca. In 2010 3rd international symposium on systems and control in aeronautics and astronautics (pp. 1169–1173).
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Lewin, A. (1999). A maintenance-free battery charge control approach for nickel-hydrogen batteries. In Proceedings of the 13th aiaa/usu conference on small satellites.
Li, D., Chen, D., Jin, B., Shi, L., Goh, J., & Ng, S.-K. (2019). Mad-gan: Multivariate anomaly detection for time series data with generative adversarial networks. In International conference on artificial neural networks (pp. 703–716).
Liu, D., Zhen, H., Kong, D., Chen, X., Zhang, L., Yuan, M., & Wang, H. (2021). Sensors anomaly detection of industrial internet of things based on isolated forest algorithm and data compression. Scientific Programming, 2021(1), 6699313.
Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation forest. In 2008 eighth ieee international conference on data mining (pp. 413–422).
Lo, N. G., Flaus, J.-M., & Adrot, O. (2019). Review of machine learning approaches in fault diagnosis applied to iot systems. In 2019 international conference on control, automation and diagnosis (iccad) (pp. 1–6).
Lou, Y. (2021). Hybrid approaches of battery performance modeling and prognosis (Unpublished doctoral dissertation). University of Michigan.
Ma, K. Y., Montoro, M., Shaw, H., Mihir, P., Woods, L., Williams, T., . . . others (2019). Utilization of unsupervised anomalies detector as a tool for managing the tdrs constellation at gsfc. In International astronautical congress (iac).
Maggipinto, M., Masiero, C., Beghi, A., & Susto, G. A. (2018). A convolutional autoencoder approach for feature extraction in virtual metrology. Procedia Manufacturing, 17, 126–133.
Maleki Sadr, M. A., Zhu, Y., & Hu, P. (2022). An anomaly detection method for satellites using monte carlo dropout. arXiv e-prints, arXiv–2211.
Mane, D. T., Sangve, S., Upadhye, G., Kandhare, S., Mohole, S., Sonar, S., & Tupare, S. (2022). Detection of anomaly using machine learning: a comprehensive survey. Int. J. Emerg. Technol. Adv. Eng., 12(11), 134–152.
Marathe, A. P. (2020). Lrz convolution: an algorithm for automatic anomaly detection in time-series data. In Proceedings of the 32nd international conference on scientific and statistical database management (pp. 1–12).
Meng, Q., Catchpoole, D., Skillicom, D., & Kennedy, P. J. (2017). Relational autoencoder for feature extraction. In 2017 international joint conference on neural networks (ijcnn) (pp. 364–371).
Mnyanghwalo, D., Kundaeli, H., Kalinga, E., & Hamisi, N. (2020). Deep learning approaches for fault detection and classifications in the electrical secondary distribution network: Methods comparison and recurrent neural network accuracy comparison. Cogent Engineering, 7(1), 1857500.
Mokhtar, A., Ibrahim, M., Hanafy, M. E., ElTohamy, F. H. A., Murtada, W. A., & Elhalwagy, Y. Z. (2024). A reliable spacecraft power supply subsystem based on discrete water cycle multi-objective optimization. Franklin Open, 7, 100092.
Mosin, V., Staron, M., Tarakanov, Y., & Durisic, D. (2022). Comparing autoencoder-based approaches for anomaly detection in highway driving scenario images. SN Applied Sciences, 4(12), 334.
Mostacciuolo, E., Vasca, F., Baccari, S., Iannelli, L., Sagnelli, S., Luisi, R., & Stanzione, V. (2019). Fault analysis to improve reliability of a leo satellite eps. IFACPapersOnLine, 52(12), 200–205.
Mutholib, A., Rahim, N. A., Gunawan, T. S., & Ahmarofi, A. A. (2024). Performance comparison of data preprocessing methods for trade-space exploration with ai model: Case study of satellite anomalies detection. In 2024 ieee 10th international conference on smart instrumentation, measurement and applications (icsima) (pp. 271–275).
Neloy, A. A., & Turgeon, M. (2024). A comprehensive study of auto-encoders for anomaly detection: Efficiency and trade-offs. Machine Learning with Applications, 100572.
Osmani, K., Haddad, A., Lemenand, T., Castanier, B., & Ramadan, M. (2020). Material-based fault detection methods for pv systems. In Key engineering materials (Vol. 865, pp. 111–115).
Peng, J., Fan, L., Xiao,W., & Tang, J. (2014). Anomaly monitoring method for key components of satellite. The Scientific World Journal, 2014(1), 104052.
Prakash, S., Venkatasubbu, S., & Konidena, B. K. (2023). From burden to advantage: Leveraging ai/ml for regulatory reporting in us banking. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 1(1), 167–176.
Purushothaman, K., Binu, B., Philip, J., Pillai, S., & Ilangovan, S. (2011). Acoustic emission monitoring and evaluation of nickel hydrogen battery cell cases. In Proceedings of the national seminar & exhibition on non-destructive evaluation (nde).
Putina, A., & Rossi, D. (2020). Online anomaly detection leveraging stream-based clustering and real-time telemetry. IEEE Transactions on Network and Service Management, 18(1), 839–854.
Raj, A., & Sharma, S. (2024). A comprehensive study on anomaly detection methods using traditional and machine learning approaches. International Journal of High School Research, 6(11).
Sakurada, M., & Yairi, T. (2014). Anomaly detection using autoencoders with nonlinear dimensionality reduction. In Proceedings of the mlsda 2014 2nd workshop on machine learning for sensory data analysis (pp. 4–11).
Sardar, B. K., Pavithra, S., Sanjay, H., & Gogoi, P. (2022). Determining stock market anomalies by using optimized z-score technique on clusters obtained from kmeans. In Recent advances in artificial intelligence and data engineering: Select proceedings of aide 2020 (pp. 401–414).
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