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High Precision Raman Distributed Fiber Sensing Using Residual Composite Dual-Convolutional Neural Network
Open accessAbstract
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 network-based denoising model for one-dimensional signals specifically tailored to Raman distributed fiber sensing. The RRCDNet-enhanced 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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“Additionally, distributed optical fibre sensors exposed to radiation can experience increased noise intensity (Guo et al., 2024;Khanam et al., 2021).”
From Impact of Sensor Degradation on Measurement Uncertainty in Prognostics and Maintenance Decision-Making: State of the art and challenges (2025) · cites this paper as Guo et al., 2024
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
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Sorted by citations
Thai, T.-T. N., Do, P., Iung, B., & Gardoni, P. (2025) · International Journal of Prognostics and Health Management · doi:10.36001/ijphm.2025.v16i4.4306 · Cited by 6
Metadata
- Authors
- Guo, H., Li, J., Xue, X., & Zhang, M.
- Affiliations
- Taiyuan University of Technology (CN)
- Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering (CN)
- Venue
- Journal of Lightwave Technology
- Published
- 2024-05-15
- Volume
- 42
- Issue
- 10
- Pages
- 3918-3928
- ISSN
- 0733-8724 (ISSN-L)
- 1558-2213
- OpenAlex
- W4391854877
- Cited by
- 18
- References
- 3
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 2
- FWCI
- 1.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Lightwave Technology (publishedVersion, cc-by-nc-nd)
- Funders
- National Natural Science Foundation of China (62205234, 62075151, 62105234)
- Fundamental Research Program of Shanxi Province (202103021223042)
- Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi
- Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Fiber Optic Sensors
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Photonic Crystal and Fiber Optics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Network Technologies
- Keywords
- temperature profile measurement, signal denoising, convolutional neural networks, signal-to-noise ratio improvement, spatial resolution
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