基于布里渊移频反分析的分布式光纤应变传感裂纹检测

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY Structural Health Monitoring-An International Journal Pub Date : 2023-11-08 DOI:10.1177/14759217231206857
Ruonan Ou, Linqing Luo, Kenichi Soga
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引用次数: 0

摘要

材料开裂是导致结构破坏的关键机制之一。分布式光纤传感(DFOS)可以沿光纤分布测量应变分布。然而,传统的布里渊- dfos系统(Brillouin optical time-domain analysis/reflectometry, BOTDA/R)的应变测量具有分米级的空间分辨率,难以测量亚毫米裂纹产生的高度局域应变。本文提出了一种基于布里渊散射谱分解的裂纹分析方法,克服了BOTDA/R系统空间分辨率诱导裂纹测量的局限性。该方法采用非负最小二乘算法反演各测点周围空间分辨率范围内的应变分布图。通过脆性矿渣水泥-水泥-膨润土梁的四点弯曲试验,验证了该方法的性能。对于窄至0.76 mm的裂缝,裂缝宽度估计误差提高到±0.005 mm。
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Distributed fiber optic strain sensing for crack detection with Brillouin shift spectrum back analysis
Material cracking is one of the key mechanisms contributing to structural failure. Distributed fiber optic sensing (DFOS) can measure the strain profile along optical fiber distributively. However, the conventional strain measurement using a Brillouin-DFOS system (Brillouin optical time-domain analysis/reflectometry (BOTDA/R)) has a decimeter-order spatial resolution, making it difficult to measure the highly localized strain generated by a sub-millimeter crack. This paper introduces a crack analysis method based on decomposing the Brillouin scattering spectrum to overcome the spatial resolution induced crack measurement limitation of the BOTDA/R system. The method uses the non-negative least squares algorithm to back-calculate the strain profile within the spatial resolution around each measurement point. The performance of this method is verified by a four point bending test of a brittle slag cement-cement-bentonite beam. The crack width estimation error is improved to ±0.005 mm for a crack as narrow as 0.76 mm.
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
期刊最新文献
Deep learning-based obstacle-avoiding autonomous UAVs with fiducial marker-based localization for structural health monitoring. Deep learning-based concrete defects classification and detection using semantic segmentation. Combination of active sensing method and data-driven approach for rubber aging detection Distributed fiber optic strain sensing for crack detection with Brillouin shift spectrum back analysis An unsupervised transfer learning approach for rolling bearing fault diagnosis based on dual pseudo-label screening
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