Optical phase mode analysis method for pipeline bolt looseness identification using distributed optical fiber acoustic sensing

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY Structural Health Monitoring-An International Journal Pub Date : 2023-08-08 DOI:10.1177/14759217231188184
Tengyu Ma, Q. Feng, Zhisen Tan, Jinping Ou
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Abstract

Distributed optical fiber acoustic sensing (DAS) technique has been applied in pipeline health monitoring, and the commonly used sensor is phase-sensitive optical time domain reflectometry. Most DAS monitoring systems can localize leakages of a pipeline but fail to identify potential non-destructive damages like bolt looseness on joints before the leakage occurs. An early damage identification is indispensable to averting severe leakages and secondary disasters. In this study, an optical phase mode analysis method is proposed for identifying pipeline bolt looseness. This method combines structure mode analysis and distributed optical phase demodulation to extract damage-related phase mode parameters. Two algorithms are specially designed for denoising and selecting signals essential for mode analysis. Phase time histories are retrieved from the original optical phase, which are decomposed to acquire phase mode shapes that can localize bolt looseness through Hilbert-Huang transform enhanced with bandwidth restricted empirical mode decomposition. Phase damping ratio is proposed to further quantify the looseness degree. Polarization diversity technique is employed to avoid polarization fading. An experiment was conducted upon a 3.2 m steel pipeline with flange joints. Bolt looseness on three joints are respectively localized even if only one bolt is loosened, obtaining a localization error of 0.07 m and 85.7% recognition ratio. The phase damping ratio shows apparent positive correlation with the number of loose bolts. The error of quantified loose bolt number is 0.79. The present study demonstrates how to localize and quantify pipeline bolt looseness through dynamical mode analysis for distributed optical phase. The developed method can identify potential damages that change the mechanical properties of a pipeline before they get severe, and holds promise in the long-distance health monitoring of other structures.
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基于分布式光纤声学传感的管道螺栓松动识别光学相位模式分析方法
分布式光纤声学传感(DAS)技术已被应用于管道健康监测,常用的传感器是相敏光学时域反射计。大多数DAS监测系统可以定位管道的泄漏,但无法在泄漏发生前识别潜在的非破坏性损伤,如接头上的螺栓松动。早期的损坏识别对于避免严重的泄漏和次生灾害是必不可少的。在本研究中,提出了一种识别管道螺栓松动的光学相位模式分析方法。该方法将结构模式分析和分布式光学相位解调相结合,提取损伤相关的相位模式参数。两种算法是专门为去噪和选择模式分析所必需的信号而设计的。从原始光学相位中提取相位时程,通过带宽受限经验模式分解增强的Hilbert-Huang变换对其进行分解,获得能够定位螺栓松动的相位模式形状。为了进一步量化松动程度,提出了相位阻尼比。为了避免偏振衰落,采用了偏振分集技术。在3.2 m钢制管道,带法兰接头。即使只有一个螺栓松动,三个接头上的螺栓松动也分别被定位,定位误差为0.07 m,识别率为85.7%。相位阻尼比与松动螺栓的数量呈明显的正相关。量化松动螺栓数量的误差为0.79。本研究演示了如何通过分布式光学相位的动态模式分析来定位和量化管道螺栓松动。所开发的方法可以在管道力学性能发生严重变化之前识别出潜在的损伤,并有望用于其他结构的远程健康监测。
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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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