概括
本研究介绍了一种增强的光频域反射计 (OFDR) 方法,使用2D卷积神经网络进行精确的应变调节. 改进的技术显著提高了测量准确度,而无需硬件更改,有利于光纤传感应用.
科学领域:
- 光电学是指光电子产品.
- 材料科学 材料科学 材料科学
- 信号处理 信号处理
背景情况:
- 光学频域反射计 (OFDR) 对于分布式应变传感至关重要.
- 传统的OFDR方法在实现高空间分辨率和大型动态应变测量范围的同时面临挑战.
- 现有的信号处理技术,如1D移动平均和1D卷积神经网络,在调节精度上有局限性.
研究的目的:
- 为OFDR提出一个改进的交叉相关应变变模块化方法.
- 在需要高空间分辨率和大动态应变测量范围的场景中提高测量精度.
- 在OFDR中克服传统的1D信号处理方法的局限性.
主要方法:
- 为OFDR开发了一种改进的交叉相关应变变调节方法.
- 转换的分布式全球光谱转换成一个二维图像矩阵.
- 采用一个无效的卷积神经网络 (CNN) 来平滑器件并重建准确的变压梯度配置文件.
主要成果:
- 与1D方法相比,实现了与5倍和2倍的改进,实现了与1D方法相比解调精度的提高,达到98.27%.
- 通过16毫米的空间分辨率,在零应变和拉伸区域中准确和清晰地测量了应变形状.
- 已确认线性应变测量,斜率为1.03和R2为0.99,适用于从100到900μm的应变.
结论:
- 拟议的基于2D CNN的OFDR方法显著提高了无需硬件修改的应变解调精度.
- 该技术有效地解决了传统的OFDR解调挑战,在苛刻的应用中实现了精确的测量.
- 这一进步有望提高光纤传感系统的性能,这些系统需要高精度和动态范围.
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