Multi-Information Fusion-Based Fault Diagnosis for Satellite Formation
Author:
Affiliation:
1. School of Electrical Engineering and Automation,Tianjin,P. R. China,300072
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10239690/10239711/10240739.pdf?arnumber=10240739
Reference11 articles.
1. Fault diagnosis and prognosis of aerospace systems using growing recurrent neural networks and lstm[C];eidali;2021 IEEE Aerospace Conference (50100),0
2. Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
3. Fault Prognosis of Satellite Reaction Wheels Using A Two-Step LSTM Network[C];islam;2021 IEEE International Conference on Prognostics and Health Management (ICPHM),0
4. Observer-based distributed fault detection and isolation for second-order multi-agent systems using relative information
5. A nonlinear observer-based approach to fault detection, isolation and estimation for satellite formation flight application
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