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Updated: Aug 8, 2025

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Spatially-resolved bending recognition based on a learning-empowered fiber specklegram sensor.

Han Gao, Haifeng Hu

    Optics Express
    |March 2, 2023
    PubMed
    Summary

    This study introduces a novel learning-empowered method for fiber specklegram bending sensors. The approach accurately identifies bending curvature and position, enhancing practical applications of fiber sensing technology.

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    Area of Science:

    • Photonics and Optical Sensing
    • Machine Learning in Sensor Technology
    • Fiber Optic Sensors

    Background:

    • Fiber specklegram sensors offer a cost-effective alternative to traditional fiber sensing technologies.
    • Existing demodulation methods for fiber specklegram sensors have limitations in measurement range and resolution.
    • Advanced signal processing is needed to unlock the full potential of fiber specklegram sensing.

    Purpose of the Study:

    • To develop a learning-empowered, spatially resolved method for fiber specklegram bending sensors.
    • To overcome the limitations of conventional demodulation schemes in terms of range and resolution.
    • To enable simultaneous identification of curvature and perturbed position using speckle pattern evolution.

    Main Methods:

    • A hybrid framework combining data dimension reduction and a regression neural network was employed.

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  • The method learns the dynamic evolution of speckle patterns within optical fibers.
  • Spatially resolved analysis of specklegrams was used for signal interrogation.

    • Main Results:

    • The proposed method achieved 100% prediction accuracy for the perturbed position.
    • Average prediction errors for curvature were 7.79 × 10⁻⁴ m⁻¹ (learned) and 7.02 × 10⁻² m⁻¹ (unlearned configurations).
    • The system demonstrated robustness and feasibility in identifying bending parameters, even for unlearned configurations.

    Conclusions:

    • The learning-empowered method significantly enhances the performance of fiber specklegram bending sensors.
    • This approach broadens the applicability of fiber specklegram sensors in real-world scenarios.
    • The study provides valuable insights into applying deep learning for interrogating sensing signals.