Retentive multimodal scale-variable anomaly detection framework with limited data groups for liquid rocket engine

Author:

Zhang Xinwei,Wang Jun,Chen Jinglong,Liu Zijun,Feng Yong

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference44 articles.

1. G. Waxenegger-Wilfing, K. Dresia, J. Deeken, and M. Oschwald, “Machine Learning Methods for the Design and Operation of Liquid Rocket Engines-Research Activities at the DLR Institute of Space Propulsion arXiv,” arXiv (USA), pp. 9 pp.-9 pp., 14 2021. [Online]. Available: ://INSPEC:20512532.

2. Z. Liu, Y. Feng, J. Chen, J. Wang, and Z. Zhang, “Intelligent anomaly detection of liquid rocket engine with multi-source data,” 07/05 2022, doi: 10.3969/j.issn.1672-9374.2022.03.010.

3. Liquid-propellant rocket engines health-monitoring - a survey;Wu;Acta Astronautica,2005

4. Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring;Schwabacher;Journal of Aerospace Computing Information and Communication,2009

5. Deep multimodal representation learning: a survey;Guo;IEEE Access,2019

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