一种用于 Φ-OTDR 系统中事件分类的对比表示学习方法
Tong Zhang1, Xinjie Peng1, Yifan Liu1
1School of Electrical and Mechanical Engineering, Pingdingshan University, Pingdingshan 467000, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
一种名为CLWTNet的新方法使用对比学习和波形变换来对 Φ-OTDR 系统中的声学事件进行分类,而不需要标记数据. 这种方法提高了数据效率,并降低了分布式声学传感的标签成本.
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:
- 阶段敏感的光学时域反射计 (Φ-OTDR) 对分布式声学传感至关重要.
- 准确的事件分类对于 Φ-OTDR 系统部署至关重要.
- 现有的方法需要大量的标记数据,这阻碍了实际应用.
研究的目的:
- 在 Φ-OTDR 系统中引入 CLWTNet,这是一种用于事件分类的新方法.
- 为了解决当前方法中标记数据依赖的瓶.
- 为 Φ-OTDR 数据分析开发一个具有成本效益和效率的解决方案.
主要方法:
- CLWTNet 在未标记的 Φ-OTDR 数据上使用对比表示学习.
- 时间域信号被转换成短时间里埃转换 (STFT) 图像.
- 波形变换卷积集成以捕获复杂的信号特征.
主要成果:
- 与监督方法相比,CLWTNet实现了具有竞争力的性能.
- 拟议的方法的性能优于现有的无监督方法.
- CLWTNet有效地从未标记的数据中提取歧视性表示.
结论:
- CLWTNet 证明了无监督表示学习对于 Φ-OTDR 事件分类的有效性.
- 该方法显著减少了对昂贵数据标签的需求.
- 对于现实世界中的 Φ-OTDR 应用,CLWTNet 提供了一个实用且高效的解决方案.
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