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Related Experiment Video

Updated: Jul 16, 2026

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings
08:23

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Published on: September 30, 2019

Grating-Based Fiber-Optic Sensing Using a Single Packaged FBG for Boundary-Dependent Motor Vibration-State

Cheng-Yu Lin1, Pei-Chung Liu1, Cheng-Kai Yao1

  • 1Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

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This study uses fiber Bragg grating (FBG) sensors to monitor motor vibration states, detecting changes in boundary conditions. The system effectively tracks relative vibration transitions without needing speed data or fault labels.

Area of Science:

  • Optical Engineering
  • Mechanical Engineering
  • Signal Processing

Background:

  • Monitoring motor-support systems requires understanding vibration states influenced by boundary conditions.
  • Traditional methods often need calibrated speed or acceleration data, limiting application in complex scenarios.

Purpose of the Study:

  • To demonstrate single-channel fiber Bragg grating (FBG) sensing for relative vibration-state monitoring.
  • To evaluate the system's performance under angle-dependent boundary conditions.
  • To assess the efficacy of machine learning models in interpreting optical vibration responses.

Main Methods:

  • A fiber Bragg grating (FBG) accelerometer-type sensor was used to record reflected Bragg wavelength fluctuations.
  • Adaptive Variational Mode Decomposition (AVMD) was applied for spectral-response tracking.
Keywords:
adaptive variational mode decompositionautoencoder alarm indicationboundary-dependent vibrationfiber Bragg gratingmotor vibration-state monitoring

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  • LSTM, 1D-CNN, and CNN-LSTM autoencoders were employed to analyze waveform departures from nominal behavior.

    • Main Results:

    • FBG sensing provided distinguishable optical vibration responses under varying boundary states.
    • Dynamic angle transitions generated local trajectory changes and potential alarm intervals.
    • The CNN-LSTM autoencoder offered a selective response, balancing transition coverage and false alarms.

    Conclusions:

    • The FBG-based framework enables compact, relative vibration-state transition monitoring.
    • This approach is suitable for applications lacking speed references, dense sensor layouts, or labeled fault data.
    • The study highlights a novel route for monitoring system dynamics in complex environments.