基于发动机试验羽毛图像的颜色和梯度特征的高斯 SVM 火焰检测模型的应用
Song Yan1, Yushan Gao2, Zhiwei Zhang1
1National Key Laboratory of Aerospace Liquid Propulsion, Xi'an Aerospace Propulsion Institute, Xi'an 710100, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
一个新的火焰检测模型使用火箭发动机测试中的图像分析来识别异常的火焰. 这种机器学习方法实现了97.6%的准确性,提高了发动机开发的安全性.
科学领域:
- 航空航天工程 航空航天工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 火箭发动机测试需要强大的监控系统来检测异常.
- 高速成像对于捕捉短暂事件,如火焰现象至关重要.
- 现有的方法可能缺乏复杂羽毛分析所需的精度.
研究的目的:
- 开发和验证液体火箭发动机的智能火焰检测模型.
- 为了利用图像处理和机器学习来检测发动机测试羽毛中的异常.
- 通过加强监测,提高火箭发动机开发的安全性和效率.
主要方法:
- 在液体火箭发动机热火试验期间收集的高速羽毛图像.
- 使用高斯支持向量机 (SVM) 具有图像特征 (颜色,渐变).
- 在YCbCr颜色空间和mRMR特征选择中使用k-means集群.
主要成果:
- 实现了97.6%的异常火焰现象检测准确度.
- 在随后的发动机测试中成功应用了该模型.
- 在识别与切除有关的异常方面表现出有效性.
结论:
- 开发的高斯 SVM 模型为火焰检测提供了一个高度准确的方法.
- 将现实世界的传感器数据与智能图像分析相结合,可以增强火箭发动机监控.
- 这种方法有助于提高火箭发动机开发的安全性和可靠性.
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