Spacecraft 3-axis Controlled Attitude Determination and Control System Reaction Wheels Fault Detection, Isolation and Identification using Machine Learning Techniques
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Published
Oct 4, 2025
T. S. Abdel Aziz
S Hussein
M. S. Mohamed
G. I. Salama
Abstract
Spacecraft attitude control systems rely on reaction wheels as the primary means of precise three-axis attitude control. Faults in these reaction wheels might lead to system instability and, in severe cases, mission failure. This paper presents advanced machine learning-based techniques for the detection, isolation, and identification of reaction wheel faults in spacecraft.
The proposed approach leverages advanced data analytics and machine learning algorithms to analyze sensor data from the reaction wheels, enabling early detection of faults and effective isolation of the faulty component and identify the types of faults detected, specifically, voltage, current and temperature faults.
Three-axis controlled satellite high-fidelity models are simulated to generate data for both nominal and faulty states of RW. The simulated data is employed with the FDII approach. The generated data is passed into five different machine learning classifiers, the isolation and identification results are verified via cross-validation. The proposed techniques is tested on three defined datasets using the three-orthogonal RW configuration to verify its robustness. The results show that the system has higher isolation and identification accuracy when compared to other studies that used various methodologies.
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References
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Abdel Aziz, T., Salama, G., Mohamed, M., & Hussein, S. (2024). Efficient machine learning based techniques for fault detection and identification in spacecraft reaction wheel. Aerospace Systems, 1–14.
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Folami, M. O. (2021). Reaction wheels fault isolation onboard 3-axis controlled satellite using enhanced random forest with multidomain features (Unpublished master’s thesis). University of Windsor (Canada).
Ismail, Z., & Varatharajoo, R. (2010). A study of reaction wheel configurations for a 3-axis satellite attitude control. Advances in Space Research, 45(6), 750-759. doi: https://doi.org/10.1016/j.asr.2009.11.004
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Omran, E. A., & Murtada, W. A. (2016). Fault detection and identification of spacecraft reaction wheels using autoregressive moving average model and neural networks. In 2016 12th international computer engineering conference (icenco) (pp. 77–82).
Omran, E. A., & Murtada, W. A. (2019). Efficient anomaly classification for spacecraft reaction wheels. Neural Computing and Applications, 31, 2741–2747.
Pachori, R. B. (2023). Time-frequency analysis techniques and their applications. CRC Press.
Podgorelec, V., & Zorman, M. (2012). Decision trees. In R. A. Meyers (Ed.), Computational complexity: Theory, techniques, and applications (pp. 827–845). New York, NY: Springer New York.
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Rahimi, A., & Saadat, A. (2019). Fault isolation of reaction wheels onboard 3-axis controlled in-orbit satellite using ensemble machine learning techniques. In The international conference on aerospace system science and engineering.
Rahimi, A., & Saadat, A. (2020). Fault isolation of reaction wheels onboard three-axis controlled in-orbit satellite using ensemble machine learning. Aerospace Systems, 3(2), 119–126.
Ramanathapuram Anand, A. (2021). Demand forecasting based on short univariate time series: A comparative study (Unpublished master’s thesis). NTNU.
Tidriri, K., Chatti, N., Verron, S., & Tiplica, T. (2016). Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges. Annual Reviews in Control, 42, 63-81. doi: https://doi.org/10.1016/j.arcontrol.2016.09.008
Vaz Carneiro, J., Schaub, H., Lahijanian, M., Lang, K., & Borozdin, K. (2022). Machine learning for reaction wheel fault detection using simulated telemetry data. In Aiaa scitech 2022 forum (p. 2507).
Voss, S. (2019). Application of deep learning for spacecraft fault detection and isolation.
Wang, R., Gong, X., Xu, M., & Li, Y. (2015). Fault detection of flywheel system based on clustering and principal component analysis. Chinese Journal of Aeronautics, 28(6), 1676–1688.
Zhu, Z., Pang, Y., & Chen, Y. (2022). A fault diagnosis method for satellite reaction wheel based on pso-elm. In 2022 41st chinese control conference (ccc) (pp. 4002–4007).
Abdel Aziz, T., Salama, G., Mohamed, M., & Hussein, S. (2024). Efficient machine learning based techniques for fault detection and identification in spacecraft reaction wheel. Aerospace Systems, 1–14.
Abd-Elhay, A.-E. R., Murtada, W. A., & Youssef, M. I. (2022). A reliable deep learning approach for time-varying faults identification: Spacecraft reaction wheel case study. IEEE Access, 10, 75495-75512. doi: 10.1109/ACCESS.2022.3191331
Akbarinia, B., & Shahmohamadi Ousaloo, H. (2023). Sensor fault-tolerant attitude determination system based on the nonlinear interacting-multiple-model approach. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 237(5), 1041–1059.
Barnett, J. T. (2001). Zero-crossings of random processes with application to estimation and detection. In F. Marvasti (Ed.), Nonuniform sampling: Theory and practice (pp. 393–437). Boston, MA: Springer US. doi: 10.1007/978-1-4615-1229-5_9
Berger, R. W., Bayles, D., Brown, R., Doyle, S., Kazemzadeh, A., Knowles, K., . . . others (2001). The rad750/sup tm/-a radiation hardened powerpc/sup tm/processor for high performance spaceborne applications. In 2001 ieee aerospace conference proceedings (cat. no. 01th8542) (Vol. 5, pp. 2263–2272).
Bill, B. (1998). High fidelity mathematical modeling of reaction wheel performance. In 21st annual american astronautical society guidance and control conference.
Cai, D., He, X., & Han, J. (2007). Srda: An efficient algorithm for large-scale discriminant analysis. IEEE transactions on knowledge and data engineering, 20(1), 1–12.
Cai, D., He, X., & Han, J. (2008). Training linear discriminant analysis in linear time. In 2008 ieee 24th international conference on data engineering (pp. 209–217).
Castaldi, P., Nozari, H., Sadati-Rostami, J., Banadaki, H., & Simani, S. (2022). Intelligent hybrid robust fault detection and isolation of reaction wheels in satellite attitude control system. In 2022 ieee 9th international workshop on metrology for aerospace (metroaerospace) (pp. 441–446).
Chaurasia, V., & Pal, S. (2020). Application of machine learning time series analysis for prediction covid-19 pandemic. Research on Biomedical Engineering, 1–13.
Dhurandhar, S. (2024). Fourier analysis. In Understanding mathematical concepts in physics: Insights from geometrical and numerical approaches (pp. 53–86). Springer.
Douc, R., Moulines, E., & Stoffer, D. (2014). Nonlinear time series: Theory, methods and applications with r examples. CRC press.
Folami, M. O. (2021). Reaction wheels fault isolation onboard 3-axis controlled satellite using enhanced random forest with multidomain features (Unpublished master’s thesis). University of Windsor (Canada).
Ismail, Z., & Varatharajoo, R. (2010). A study of reaction wheel configurations for a 3-axis satellite attitude control. Advances in Space Research, 45(6), 750-759. doi: https://doi.org/10.1016/j.asr.2009.11.004
Karlöf, L., Ølgård, T., Godtliebsen, F., Kaczmarska, M., & Fischer, H. (2005). Statistical techniques to select detection thresholds for peak signals in ice-core data. Journal of Glaciology, 51(175), 655–662. doi: 10.3189/172756505781829115
Kraja, F., & Acher, G. (2011). Using many-core processors to improve the performance of space computing platforms. In 2011 aerospace conference (pp. 1–17).
Lee, R., & CHEN, I.-Y. (2020). The time complexity analysis of neural network model configurations. In 2020 international conference on mathematics and computers in science and engineering (macise) (pp. 178–183).
Lopez-Villalobos, C. A., Rodriguez-Hernandez, O., Martinez-Alvarado, O., & Hernandez-Yepes, J. (2021). Effects of wind power spectrum analysis over resource assessment. Renewable Energy, 167, 761–773.
Mansell, J. R. (2020). Deep learning fault protection applied to spacecraft attitude determination and control (Unpublished doctoral dissertation). Purdue University.
Müller, M. (2015). Fundamentals of music processing: Audio, analysis, algorithms, applications (Vol. 5). Springer.
Nalepa, J., & Kawulok, M. (2019). Selecting training sets for support vector machines: a review. Artificial Intelligence Review, 52(2), 857–900.
Ni, S., Chen, S., Liao, Y., & Cheng, N. (2021). Design and verification of attitude control system for a small satellite. In Proceedings of the 2021 2nd international conference on control, robotics and intelligent system (p. 88–92). New York, NY, USA: Association for Computing Machinery. Retrieved from https://doi.org/10.1145/3483845.3483861 doi: $10.1145/3483845.3483861$
Nomura, S., Ikari, S., & Nakasuka, S. (2016). Three-axis attitude maneuver of spacecraft by reaction wheels with rotation speed constraints. IFAC-PapersOnLine, 49(17) 130-134. (20th IFAC Symposium on Automatic Control in AerospaceACA 2016) doi: https://doi.org/10.1016/j.ifacol.2016.09.023
Omran, E. A., & Murtada, W. A. (2016). Fault detection and identification of spacecraft reaction wheels using autoregressive moving average model and neural networks. In 2016 12th international computer engineering conference (icenco) (pp. 77–82).
Omran, E. A., & Murtada, W. A. (2019). Efficient anomaly classification for spacecraft reaction wheels. Neural Computing and Applications, 31, 2741–2747.
Pachori, R. B. (2023). Time-frequency analysis techniques and their applications. CRC Press.
Podgorelec, V., & Zorman, M. (2012). Decision trees. In R. A. Meyers (Ed.), Computational complexity: Theory, techniques, and applications (pp. 827–845). New York, NY: Springer New York.
Rahimi, A., Datta, S., Kleyko, D., Frady, E. P., Olshausen, B., Kanerva, P., & Rabaey, J. M. (2017). High-dimensional computing as a nanoscalable paradigm. IEEE Transactions on Circuits and Systems I: Regular Papers, 64(9), 2508–2521.
Rahimi, A., & Saadat, A. (2019). Fault isolation of reaction wheels onboard 3-axis controlled in-orbit satellite using ensemble machine learning techniques. In The international conference on aerospace system science and engineering.
Rahimi, A., & Saadat, A. (2020). Fault isolation of reaction wheels onboard three-axis controlled in-orbit satellite using ensemble machine learning. Aerospace Systems, 3(2), 119–126.
Ramanathapuram Anand, A. (2021). Demand forecasting based on short univariate time series: A comparative study (Unpublished master’s thesis). NTNU.
Tidriri, K., Chatti, N., Verron, S., & Tiplica, T. (2016). Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges. Annual Reviews in Control, 42, 63-81. doi: https://doi.org/10.1016/j.arcontrol.2016.09.008
Vaz Carneiro, J., Schaub, H., Lahijanian, M., Lang, K., & Borozdin, K. (2022). Machine learning for reaction wheel fault detection using simulated telemetry data. In Aiaa scitech 2022 forum (p. 2507).
Voss, S. (2019). Application of deep learning for spacecraft fault detection and isolation.
Wang, R., Gong, X., Xu, M., & Li, Y. (2015). Fault detection of flywheel system based on clustering and principal component analysis. Chinese Journal of Aeronautics, 28(6), 1676–1688.
Zhu, Z., Pang, Y., & Chen, Y. (2022). A fault diagnosis method for satellite reaction wheel based on pso-elm. In 2022 41st chinese control conference (ccc) (pp. 4002–4007).
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