概括
本研究介绍了一种高效的多维特征提取网络,用于光纤振动信号识别. 这种新方法在分类振动信号方面实现了98.67%的高平均识别率.
科学领域:
- 光纤传感传感器是指光纤传感器.
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 一维光纤振动信号具有有限的特征,阻碍了浅层神经网络的识别准确性.
- 现有的复杂算法和数据处理方法使光学振动信号识别复杂化.
研究的目的:
- 为改进光纤振动信号识别提出一个高效的多维特征提取网络.
- 为了增强特征提取能力,以便更有效地进行振动信号分类.
主要方法:
- 使用了ResNet-50,具有高效的频道注意力 (ECA),用于改进图像特征提取.
- 集成了长期短期记忆 (LSTM) 网络,以增强时间特征提取.
- 将一维的振动信号转换为128x128灰度图像,以获得更丰富的信息.
主要成果:
- 成功地采集和处理了三种不同的振动信号,使用相位敏感的光学时域反射计 (Φ-OTDR) 系统.
- 拟议的网络有效地识别和分类了不同类型的振动信号.
- 实现了 98.67% 的高平均识别率.
结论:
- 提出的高效的多维特征提取网络显著改善光纤振动信号识别.
- 将信号转换为灰度图像可以增强信息内容,以便更好地进行分类.
- 集成ECA和LSTM网络为复杂的振动信号分析提供了强大的解决方案.
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