分布式声传感
计算机科学
光纤
光纤传感器
光缆
拉曼放大
光纤到x
电信
光时域反射计
光纤分路器
无源光网络
电子工程
拉曼散射
材料科学
波分复用
光学
工程类
拉曼光谱
光电子学
波长
物理
作者
Glenn A. Wellbrock,Tiejun J. Xia,Ming-Fang Huang,Shaobo Han,Yuheng Chen,Ting Wang,Yoshiaki Aono
标识
DOI:10.1109/jlt.2023.3263795
摘要
We review various applications of distributed fiber optic sensing (DFOS) and machine learning (ML) technologies that particularly benefit telecom operators' fiber networks and businesses. By leveraging relative phase shift of the reflectance of coherent Rayleigh, Brillouin and Raman scattering of light wave, the ambient environmental vibration, acoustic effects, temperature and fiber/cable strain can be detected. Fiber optic sensing technology allows optical fiber to support sensing features in addition to its conventional role to transmit data in telecommunications. DFOS has recently helped telecom operators by adding multiple sensing features and proved feasibility of co-existence of sensing and communication systems on same fiber. We review the architecture of DFOS technique, and show examples where optical fiber sensing helps enhance network operation efficiency and create new services for customers on deployed fiber infrastructures, such as determination of cable locations, cable cut prevention, perimeter intrusion detection and networked sensing applications. In addition, edge AI platform allows data processing to be conducted on-the-fly with low latency. Based on discriminative spatial-temporal signatures of different events of interest, real-time processing of the sensing data from the DFOS system provides results of the detection, classification and localization immediately.
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