基于EMD算法和CNN-LSTM的光纤振动信号识别
1School of Electronic Information Engineering, Anhui University, Hefei 230601, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究引入了一种新方法,将实证模式分解 (EMD) 与卷积神经网络 (CNN) 和长短期记忆 (LSTM) 网络相结合,用于准确识别光纤振动信号. 该方法在检测入侵信号方面达到97.3%的准确性,增强了周边安全系统.
科学领域:
- 光电学是指光电子产品.
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 精确识别光纤振动信号对于周边安全系统至关重要.
- 使用相位敏感光时域反射计 (φ-OTDR) 的分布式声学传感 (DAS) 是入侵检测的关键技术.
- 在φ-OTDR系统中提高入侵事件的识别精度仍然是一个挑战.
研究的目的:
- 为了提高 φ-OTDR 系统检测到的入侵事件的识别精度.
- 提出一种结合EMD,CNN和LSTM的新型识别方法.
- 在现实环境中验证拟议方法的有效性.
主要方法:
- 光纤振动信号使用实证模式分解 (EMD) 进行了分解.
- 有效的内在模式函数 (IMFs) 根据相关系数进行选择和重建.
- 卷积神经网络 (CNN) 提取时间序列特征,长短期记忆 (LSTM) 网络分类信号.
主要成果:
- 拟议的EMD-CNN-LSTM方法有效地识别了三种不同类型的振动信号.
- 对于入侵信号,实现了97.3%的识别精度.
- 该方法证明了 φ-OTDR 系统的成功模式识别.
结论:
- 结合EMD-CNN-LSTM方法显著提高了φ-OTDR系统中的入侵检测准确性.
- 这种方法为开发外围安全的实用工程产品提供了宝贵的见解.
- 这项研究成功地解决了φ-OTDR模式识别方面的挑战.
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