基于凸优化信息融合的火箭发动机异常检测方法
Hao Sun1, Yuehua Cheng1, Bin Jiang1
1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
这项研究引入了一种新的数据融合算法,用于探测火箭发动机异常,提高可靠性和安全性. 该方法通过将发动机测量与火箭飞行数据相结合,提高了检测准确度和速度.
科学领域:
- 航空航天工程 航空航天工程
- 系统安全系统安全系统安全系统
- 数据科学数据科学数据科学
背景情况:
- 火箭动力系统对于发射器的可靠性和安全性至关重要.
- 有限的发动机传感器数据阻碍了快速准确的异常检测.
- 整合多种数据源对于稳健的监测至关重要.
研究的目的:
- 为增强火箭发动机异常检测开发一个最佳的数据融合方案.
- 为了提高飞行过程中识别发动机故障的速度和准确性.
- 为了利用火箭飞行状态数据与发动机测量一起.
主要方法:
- 提出了一种基于凸优化的数据集信息融合算法.
- 使用凸二次编程来确定最佳的融合参数.
- 使用自适应CUSUM算法来检测发动机故障的异常.
主要成果:
- 与传统方法相比,拟议的算法显示了更高的检测准确性.
- 实现了较短的发动机故障检测时间.
- 成功地整合了来自发动机测量和飞行状态的不同数据分布.
结论:
- 新的数据融合算法显著提高了火箭发动机异常检测能力.
- 整合飞行状态数据提供了一个有价值的,扩展的信息来源.
- 这种方法为提高发射器安全性和可靠性提供了一个有希望的解决方案.
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