The propulsion system in a spacecraft is an important subsystem for orbit transfer and attitude control. A fast and accurate fault diagnosis system contributes to the safety of the entire system. As the system becomes more complex, identifying faults, their locations, and root causes becomes increasingly difficult. This study utilized Principal Component Analysis (PCA) and feature optimization with Fast Fourier Transform (FFT) analysis using greedy algorithm to achieve fault diagnosis systems for spacecraft to replace the current operation based on the expert knowledge. By applying PCA to simulation data for the faults were successfully detected and their locations and root causes identified.
Spacecraft, Propulsion System, PHM
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 amp; data mining (p. 387–395). New York, NY, USA: Association for Computing Machinery. Retrieved from https://doi.org/10.1145/3219819.3219845 doi: 10.1145/3219819.321
Kawatsu, K., Noumi, A., Ishihama, N., Nagata, T., Inoue, C., Fujii, G., Daimon, Y. (2020). Resilient redundant spacecraft gn&c system fault detection and diagnostics. In of the aerospace europe conference, aec2.
Khan, S., & Yairi, T. (2018). A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 107, 241265.
Modelica Association, Modelica Language Specification, https://www.modelica.org/documents (accessed April 1, 2023).
Saruta, K., Tsukimori, K., Shimada, Y., Nishimura, A., & Kobayashi, T. (2010). Development of a health monitoring system using thermally stable fiber bragg gratings for fast reactor power plants: Experimental demonstration of strain measurement. JAEA Conf , 149-15
SimulationX, https://www.simulationx.com/ (accessed April 1, 2023).
Tominaga, K., Daimon, Y., Toyama, M., Adachi, K., Tsutsumi, S., Omata, N., & Nagata, T. (2023). Dataset generation based on 1D-CAE modeling for fault diagnostics in a spacecraft propulsion system. To be published.
Tominaga, K., Fujii, G., Nagata, T., Wada, D., Hisada, S., Kawatsu, K., & Kasai, T. (2022). Anomaly detection method of spacecraft propulsion using multiplexed fiber bragg grating. 9th European Conference for Aeronautics and Space Science, https://doi.org/10.13009/EUCASS2022-6177 doi: 10.13009/EUCASS2022-6177
Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and intelligent laboratory systems, 2(1-3), 37–52
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 proba4 Asia Pacific Conference of the Prognostics and Health Management Society 2023 bilistic clustering and dimensionality reduction. IEEE Transactions on Aerospace and Electronic Systems, 53(3), 1384-1401. doi: 10.1109/TAES.2017.2
This work is licensed under a Creative Commons Attribution 3.0 Unported License.