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使用低水峰光纤和优化去噪神经网络的24公里高性能拉曼分布式温度传感

24 km High-Performance Raman Distributed Temperature Sensing Using Low Water Peak Fiber and Optimized Denoising Neural Network.

作者信息

Wu Hao, Du Haoze, Zhao Can, Tang Ming

机构信息

Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.

出版信息

Sensors (Basel). 2022 Mar 10;22(6):2139. doi: 10.3390/s22062139.

Abstract

Raman distributed optical fiber temperature sensing (RDTS) has been extensively studied for decades because it enables accurate temperature measurements over long distances. The signal-to-noise ratio (SNR) is the main factor limiting the sensing distance and temperature accuracy of RDTS. We manufacture a low water peak optical fiber (LWPF) with low transmission loss to improve the SNR for long-distance application. Additionally, an optimized denoising neural network algorithm is developed to reduce noise and improve temperature accuracy. Finally, a maximum temperature uncertainty of 1.77 °C is achieved over a 24 km LWPF with a 1 m spatial resolution and a 1 s averaging time.

摘要

拉曼分布式光纤温度传感(RDTS)已经被广泛研究了数十年,因为它能够实现长距离的精确温度测量。信噪比(SNR)是限制RDTS传感距离和温度精度的主要因素。我们制造了一种具有低传输损耗的低水峰光纤(LWPF),以提高长距离应用的信噪比。此外,还开发了一种优化的去噪神经网络算法,以降低噪声并提高温度精度。最后,在一根24公里长、空间分辨率为1米、平均时间为1秒的LWPF上,实现了1.77°C的最大温度不确定度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/28ce/8949466/240a82ab4688/sensors-22-02139-g009.jpg

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