Anomaly detection of propulsion system of spacecrafts with Light GBM



Published Sep 4, 2023
Shota Iino Hideki Nomoto Takayuki Hirose Yasutaka Michiura Go Fujii Takashi Uchiyama


Future spacecrafts require robust operations for long-term missions to the Moon or Mars. Automatic anomaly detection with machine learning, in this context, plays a significant role because it enables early symptom detection and proactive redundant switching which preserves components in the long mission. In this research, we adopted Light GBM, one of the machine learning models, to investigate such anomaly. We especially focused on the telemetry data of propulsion system of H-II Transfer Vehicle (HTV) to resolve typical problems of deep-space mission spacecrafts, a thruster failure. The data was collected from multiple types of thruster maneuvers performed at simulator training. The results showed the effectiveness of the proposed method.  

Abstract 97 | PDF Downloads 131



anomaly detection, safety, aerospace, propulsion, robustness, machine learning

Hayton, P., Utete, S., King, D., King, S., Anuzis, P., & Tarassenko, L. (2007). Static and dynamic novelty detection methods for jet engine health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851), 493-514

Hotta, S., Yamanaka, K., Wada, K., & Kawashima, I. (2012). H-II transfer vehicle trajectory planning and Flight operation results. In The 23rd International Symposium on Space Flight Dynamics.

Iverson, D. L., Martin, R., Schwabacher, M., Spirkovska, L., Taylor, W., Mackey, R., ... & Baskaran, V. (2012). General purpose data-driven monitoring for space operations. Journal of Aerospace Computing, Information, and Communication, 9(2), 26-44

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30

Schwabacher, M., Oza, N., & Matthews, B. (2009). Unsupervised anomaly detection for liquid-fueled rocket propulsion health monitoring. Journal of aerospace computing, information, and communication, 6(7), 464 482

Rasmussen, J. (1983). Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models. IEEE transactions on systems, man, and cybernetics, (3), 257-266
Special Session Papers