强大的向量BOTDA信号处理与概率机器学习
Abhishek Venketeswaran1, Nageswara Lalam1,2, Ping Lu1,2
1National Energy Technology Laboratory, 626 Cochrans Mill Road, Pittsburgh, PA 15236, USA.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
一个新的概率机器学习 (PML) 框架准确地估计了矢量Brillouin光学时域分析 (VBOTDA) 系统中的Brillouin频率偏移 (BFS). 该方法还量化了预测不确定性,提高了VBOTDA的性能.
科学领域:
- 光电学是指光电子产品.
- 机器学习 机器学习
- 光纤传感传感器是指光纤传感器.
背景情况:
- 矢量Brillouin光学时域分析 (VBOTDA) 对于光纤传感至关重要.
- 准确估计Brillouin频率转移 (BFS) 对VBOTDA的性能至关重要.
- 目前用于BFS估计的方法可能缺乏准确性和不确定性评估.
研究的目的:
- 在VBOTDA中引入一种新的概率机器学习 (PML) 框架,用于BFS估计.
- 评估与基于PML的BFS估计相关的预测不确定性.
- 将PML框架的性能与传统方法进行比较.
主要方法:
- 开发一个概率机器学习 (PML) 框架.
- 应用PML框架来估计BFS从Brillouin增益和阶段光谱.
- 与传统的曲线拟合方法进行比较.
- 使用两个BOTDA系统 (10公里和25公里光纤) 验证.
主要成果:
- 该PML框架准确地预测了沿传感纤维的BFS.
- 该框架提供了对BFS预测不确定性的可靠评估.
- 在10公里和25公里VBOTDA系统中证明了有效性.
- 与传统方法相比,可能减少数据处理时间.
结论:
- 拟议的PML框架为VBOTDA中的BFS估计提供了一个强大的方法.
- 这种方法通过提供准确的BFS预测和不确定性量化来提高VBOTDA系统性能.
- 对于推进光纤传感技术来说,PML是一个有前途的方向.
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