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Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
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在多模光纤中进行光斑解码的温度不敏感的应变识别.

Hanchao Sun, Jixuan Wu, Binbin Song

    Optics letters
    |November 1, 2024
    PubMed
    概括

    一个新的光纤应变传感器使用深度学习来解码斑点图案,达到99.28%的准确性. 这种对温度不敏感的系统克服了交叉灵敏度,用于工程应用中可靠的应变测量.

    科学领域:

    • 光电学是指光电子产品.
    • 光纤传感传感器是指光纤传感器.
    • 机器学习是机器学习.

    背景情况:

    • 光纤传感器对于测量物理和生化参数至关重要.
    • 温度交叉敏感性是实际光纤传感应用中的一个主要局限性.
    • 精确的应变测量在各种工程领域至关重要.

    研究的目的:

    • 开发一种温度不敏感的光纤应变传感器.
    • 利用深度学习来增强光学传感器中的信号识别.
    • 为了应对应变传感中温度交叉灵敏性的挑战.

    主要方法:

    • 采用了基于散射模式的斑点解码感应机制.
    • 一个深度学习算法被利用来分析斑点模式来估计应变.
    • 进行了实验验证,以评估在不同温度和应变下传感器性能.

    主要成果:

    • 拟议的传感器证明了对温度不敏感的应变测量.
    • 一个分类模型实现了99.28%的识别精度,用于轴向应变 (0-0.3 N).
    • 应变预测的平均平方根平均误差为1.02N%.

    结论:

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    • 智能斑点传感策略有效地减轻了温度交叉敏感性.
    • 基于深度学习的斑点图案分析能够实现准确可靠的应变传感.
    • 这种方法对于在工程领域推进光纤传感器应用具有重大潜力.