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基于声辐射的智能识别活塞航空发动机点火前进角度异常
Yanhe Yang1, Xiaoyang Bi2,3, Alamusi Lee4
1School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, People's Republic of China.
本研究介绍了一种智能方法,使用声辐射信号来检测发动机中的异常点火前进角度. 该技术实现了高精度,优于传统方法,用于改进发动机诊断.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 异常的点火前进角度显著降低了发动机性能,并可能造成严重损坏.
- 传统的诊断方法与发动机系统的复杂和封闭性质作斗争.
研究的目的:
- 开发一种智能方法,准确识别发动机中异常点火前进角度.
- 克服传统诊断方法的局限性.
主要方法:
- 使用了从发动机表面收集的声辐射信号.
- 处理AE信号光谱,使用最小距离方法选择高贡献频段,以创建特征图.
- 采用卷积神经网络 (CNN) 用于使用构建的特征地图进行分类.
主要成果:
- 在正常的活塞航空发动机运行条件下,在识别异常点火前进角度时达到100%的准确性.
- 在其他两种操作条件下,已经证明了99.75%和98.5%的高识别精度.
- 与传统技术相比,拟议的方法显示出更高的性能.
结论:
- 基于AE信号和CNN的智能识别方法在检测异常的点火前进角度方面非常有效.
- 功能提取和地图构造提高了故障信息的可见性.
- 该方法显示出普遍性和实用发动机诊断的重大潜力.
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