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基于多芯光纤的单端自校准高精度拉曼分布式温度传感

Single-ended self-calibration high-accuracy Raman distributed temperature sensing based on multi-core fiber.

作者信息

Du Haoze, Wu Hao, Zhang ZhongShu, Zhao Can, Zhao Zhiyong, Tang Ming

出版信息

Opt Express. 2021 Oct 11;29(21):34762-34769. doi: 10.1364/OE.440265.

Abstract

Raman distributed temperature sensing (RDTS) obtains the temperature information by measuring the intensities of Raman scattering lights. The anti-stokes only RDTS can avoid the error caused by wavelength-dependent loss and dispersion. However, to eliminate temperature-independent intensity variations, single-wavelength demodulation generally adopts the double-ended detection scheme. This requires two optical fibers or one fiber to be folded into a loop, which is inconvenient in practical applications. Moreover, the temperature accuracy of such a scheme is lower than the conventional single-ended system, so it has not been widely used. Here, we propose and experimentally demonstrate a multi-core fiber (MCF) based RDTS system. A single-ended loop structure is achieved by connecting two cores at the far end of the MCF with a fan-in/fan-out device. By measuring the backscattered anti-stokes lights in the two cores, the results can be self-calibrated to eliminate the influence of temperature-independent light intensity changes. Besides, the results can be improved by averaging the temperatures of the two cores due to the spatial consistency of the MCF. Moreover, to further improve the temperature uncertainty, we employ the one-dimensional denoising convolutional neural network. Finally, a maximum temperature uncertainty of 1.4 °C is achieved over a 10 km MCF with a 3 m spatial resolution.

摘要

拉曼分布式温度传感(RDTS)通过测量拉曼散射光的强度来获取温度信息。仅采用反斯托克斯光的RDTS可以避免由波长相关损耗和色散引起的误差。然而,为了消除与温度无关的强度变化,单波长解调通常采用双端检测方案。这需要两根光纤或一根光纤折成一个环,在实际应用中不太方便。此外,这种方案的温度精度低于传统的单端系统,因此尚未得到广泛应用。在此,我们提出并通过实验证明了一种基于多芯光纤(MCF)的RDTS系统。通过在MCF的远端用一个扇入/扇出装置连接两个芯来实现单端环结构。通过测量两个芯中的背向散射反斯托克斯光,结果可以进行自校准,以消除与温度无关的光强变化的影响。此外,由于MCF的空间一致性,通过对两个芯的温度进行平均可以提高结果。而且,为了进一步降低温度不确定度,我们采用了一维去噪卷积神经网络。最后,在一根10公里长、空间分辨率为3米的MCF上实现了1.4°C的最大温度不确定度。

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