TY - GEN
T1 - Digital Twin-Driven Degradation Modeling Method for Control Moment Gyroscope Health Management
AU - Cui, Runhao
AU - Huang, Xucong
AU - Zhang, Peng
AU - Tang, Diyin
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Prognosis and health management (PHM) of Control moment gyroscope (CMG) plays a crucial role in ensuring the operational efficiency and safety of spacecraft. In order to improve the accuracy of PHM and supplement abundant monitoring data, this paper proposes a digital twin-driven degradation modeling method, which establishes a detailed simulation model based on degradation mechanisms at the CMG component level. The digital twin model not only provides a large amount of high-quality data for performance evaluation, but also serves as an important reference for on-orbit CMG state assessment. Finally, a case of estimating virtual sensor degradation information based on reinforcement learning is used to demonstrate the effectiveness of the proposed digital twin method.
AB - Prognosis and health management (PHM) of Control moment gyroscope (CMG) plays a crucial role in ensuring the operational efficiency and safety of spacecraft. In order to improve the accuracy of PHM and supplement abundant monitoring data, this paper proposes a digital twin-driven degradation modeling method, which establishes a detailed simulation model based on degradation mechanisms at the CMG component level. The digital twin model not only provides a large amount of high-quality data for performance evaluation, but also serves as an important reference for on-orbit CMG state assessment. Finally, a case of estimating virtual sensor degradation information based on reinforcement learning is used to demonstrate the effectiveness of the proposed digital twin method.
KW - Control Moment Gyroscope
KW - Digital Twin Model
KW - Prognosis and Health Management
UR - https://www.scopus.com/pages/publications/85173055419
U2 - 10.1109/BigDataService58306.2023.00049
DO - 10.1109/BigDataService58306.2023.00049
M3 - 会议稿件
AN - SCOPUS:85173055419
T3 - Proceedings - IEEE 9th International Conference on Big Data Computing Service and Applications, BigDataService 2023
SP - 236
EP - 241
BT - Proceedings - IEEE 9th International Conference on Big Data Computing Service and Applications, BigDataService 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2023
Y2 - 17 July 2023 through 20 July 2023
ER -