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Updated: Dec 25, 2025

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings
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Fiber Bragg Grating Dynamic Calibration Based on Online Sequential Extreme Learning Machine.

Qiufeng Shang1, Wenjie Qin1

  • 1Department of Electronic and Communication Engineering, North China Electric Power University, No. 619 Yong Hua Street, Baoding 071003, China.

Sensors (Basel, Switzerland)
|April 1, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic calibration method for fiber Bragg grating (FBG) sensors using online sequential extreme learning machine (OS-ELM). This approach enhances real-time temperature measurement accuracy and long-term stability by continuously updating the calibration model.

Keywords:
dynamic calibrationfiber Bragg gratingsonline sequential extreme learning machineoptical fiber sensors

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Area of Science:

  • Sensor Technology
  • Machine Learning
  • Data Science

Background:

  • Fiber Bragg Grating (FBG) sensors are crucial for accurate measurements.
  • Traditional static calibration methods have limitations in real-time accuracy and long-term stability.
  • Dynamic calibration is needed to address these limitations.

Purpose of the Study:

  • To present a novel dynamic calibration method for FBG sensors.
  • To improve the real-time accuracy and predictive speed of FBG temperature measurements.
  • To compare the proposed method with traditional calibration techniques.

Main Methods:

  • Utilized the online sequential extreme learning machine (OS-ELM) for dynamic calibration.
  • Implemented continuous model updating instead of complete retraining.
  • Compared OS-ELM with polynomial fitting, back propagation (BP) network, and radial basis function (RBF) network.

Main Results:

  • The dynamic OS-ELM method demonstrated superior generalization performance compared to static methods.
  • The proposed calibration approach exhibited a faster learning process.
  • Continuous online learning addressed the insufficient coverage of static calibration training samples.

Conclusions:

  • The dynamic calibration method significantly improves the long-term stability and prediction accuracy of FBG sensors.
  • OS-ELM offers an efficient and effective solution for real-time FBG sensor calibration.
  • This approach enhances the overall performance and reliability of FBG sensing systems.