Research Progress of Intelligent Health Monitoring Technology for Liquid-Propellant Rocket Engines

被引：0
|
作者：
Wu J.-J. [1 ]
Zhu X.-B. [1 ]
Cheng Y.-Q. [1 ]
Cui M.-Y. [1 ]
机构：
[1] College of Aerospace Science and Engineering, National University of Defense Technology, Changsha
来源：
Tuijin Jishu/Journal of Propulsion Technology | 2022年 / 43卷 / 01期
关键词：
Artificial intelligence; Fault detection and diagnosis; Health monitoring; Liquid-propellant rocket engine; System;
D O I：
10.13675/j.cnki.tjjs.200668
中图分类号：
学科分类号：
摘要：
Taken as the core and key technology for improving and enhancing the reliability and security of liquid-propellant rocket engines, health monitoring technology has a significant trend of intelligent development in recent years. In this paper, a brief history of health monitoring technology has been summarized firstly. After that, intelligent fault detection and diagnosis methods are systematically introduced focusing on the methods based on traditional artificial intelligence and deep learning. The research status of health monitoring system in schematic design and practical application is further elaborated, and the development of intelligent health monitoring system is introduced on this basis. Finally, some suggestions and prospects are given on the development trend of health monitoring technology. © 2022, Editorial Department of Journal of Propulsion Technology. All right reserved.
引用
收藏
页码：1 / 13
页数：12
相关论文
共 104 条
  • [1] (2017)
  • [2] (2004)
  • [3] 39, 6, (2013)
  • [4] Zhang W, Tian G, Xu Z, Et al., Failure Characteristics Analysis and Fault Diagnosis for Liquid Rocket Engines, (2016)
  • [5] Zhao Y, Li T, Zhang X, Et al., Artificial Intelligence-Based Fault Detection and Diagnosis Methods for Building Energy Systems: Advantages, Challenges and the Future, Renewable and Sustainable Energy Reviews, 109, pp. 85-101, (2019)
  • [6] Goodfellow I, Bengio Y, Courville A., Deep Learning, (2016)
  • [7] Michie D, Spiegelhalter D J, Taylor C C, Et al., Machine Learning, Neural and Statistical Classification, (1995)
  • [8] Iverson D L., Inductive System Health Monitoring, Proceedings of the International Conference on Artificial Intelligence, (2004)
  • [9] Schwabacher M, Nikunj C O, Matthews B, Et al., Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring, Journal of Aerospace Computing Information & Communication, 6, pp. 464-482, (2009)
  • [10] (2016)
12345678910