CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13107 > Article
Paper
6 May 2024 Research on spacecraft anomaly detection method based on neural networks
Hongfei Li, Mingjiang Zhang, Xiangyan Zhang, Zhiqiang Li, Huadong Tian
Author Affiliations +
Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 1310709 (2024) https://doi.org/10.1117/12.3029308
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China
Abstract
The monitoring of telemetry data of spacecraft in orbit is of great significance for fault detection and early warning. The existing spacecraft monitoring methods are mainly based on rule knowledge and do not make good use of historical data, especially for multi parameter system anomalies. In this paper, a method of anomaly detection based on long-short term memory network(LSTM) which has memory effect is proposed for spacecraft, especially for dynamic parameters. Firstly, a set of telemetry data preprocessing methods are proposed. Secondly, for the modeling of complex system, Pearson correction coefficient method is proposed to reduce the dimension of telemetry parameters. Then, for dynamic parameters, the modeling methods of LSTM neural network are proposed, and the process of anomaly monitoring is given. Finally, using the historical telemetry data of a system, the modeling effect of neural network is analyzed. The results show that the estimated value of the model is consistent with the measured value, and the abnormal changes of the system can be found effectively.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Hongfei Li, Mingjiang Zhang, Xiangyan Zhang, Zhiqiang Li, and Huadong Tian "Research on spacecraft anomaly detection method based on neural networks", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 1310709 (6 May 2024); https://doi.org/10.1117/12.3029308
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
;
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 8 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Neural networks
Modeling
Machine learning
Matrices
Systems modeling
Statistical analysis
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top