Enhanced Raman Distributed Temperature Sensor Based on Self-Constructed Fully Connected Neural Network

Lin, Shan; Chang, Jun; Zhang, Zhifeng; Sun, Jiachen; Huo, Dianheng; Mao, Minghui; Wang, Zihan

doi:10.1109/jsen.2022.3183014

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…But these denoising algorithms have the problems of requiring manual adjustment of parameters, having poor denoising performance, and deteriorating the spatial resolution, a key system metric that is susceptible to degradation during the denoising process. In order to solve the aforementioned problems, neural network-based denoising algorithms have been proposed [38], [39].…”

Section: Introductionmentioning

confidence: 99%

High Precision Raman Distributed Fiber Sensing Using Residual Composite Dual-Convolutional Neural Network

Guo,

Li,

Xue

et al. 2024

J. Lightwave Technol.

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Raman distributed optical fiber sensing has the unique ability to measure the spatially distributed profile of temperature that are of great interest to numerous field applications. However, the sensing performance is severely limited by the signal-to-noise ratio (SNR). The existing SNR enhancement schemes have drawbacks such as increased system complexity, degradation of sensor performance metrics such as spatial resolution, poor denoising performance, etc. Here, we report the Raman residual composite dual-convolutional neural network (RRCDNet), a novel convolutional neural networkbased denoising model for one-dimensional signals specifically tailored to Raman distributed fiber sensing. The RRCDNetenhanced Raman distributed fiber sensor system dramatically improves the temperature precision by more than a factor of 100, from 7.57°C to 0.06°C, without hardware modification or degradation of other performance metrics. At the same time, RRCDNet can also enhance other optical fiber sensor systems with one-dimensional signals, such as Rayleigh and Brillouin sensing systems.

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Section: Introductionmentioning

confidence: 99%