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.
Highlights
As a result, distributed optical fiber temperature sensing is widely employed in power grids, oil pipelines, nuclear power facilities, and other applications [2–5]
We propose and experimentally demonstrate an Raman distributed optical fiber temperature sensing (RDTS) system based on a single-mode low water peak optical fiber (LWPF)
We show the result using wavelet denoising (WD), which is a conventional method for processing RDTS signals [14]
Summary
Since the 1970s, with the birth of low-loss optical fibers and the wide application of lasers, research on optical fibers has been greatly expanded [1]. Average time, temperature accuracy, and spatial resolution are the indicators used to evaluate the RDTS system’s performance. In. 2021, Yang et al increased temperature uncertainty to 0.5 ◦ C with single-mode Raman gain fiber (RGF), with the system parameters set to a sensing distance of 2.9 km, a spatial resolution of 3 m, and a measurement time of 15 s [11]. 2021, Yang et al increased temperature uncertainty to 0.5 ◦ C with single-mode Raman gain fiber (RGF), with the system parameters set to a sensing distance of 2.9 km, a spatial resolution of 3 m, and a measurement time of 15 s [11] These special fibers improve the SNR by increasing the input power or the Raman scattering coefficient. By denoising the AS curve with the optimized neural network, an LWPF RDTS system with high temperature accuracy and a long sensing distance is realized
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