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IR Frequency Region: Fingerprint Region01:03

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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A Deep Learning Framework for Enhancing High-Frequency Optical Fiber Vibration Sensing from Low-Sampling-Rate FBG

Mentari Putri Jati1,2, Cheng-Kai Yao1, Yen-Chih Wu1

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

Sensors (Basel, Switzerland)
|July 12, 2025
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Summary
This summary is machine-generated.

A new deep neural network (DNN) framework overcomes low sampling rate limits for fiber Bragg grating (FBG) sensors. This enables accurate high-frequency vibration recognition from raw signals, advancing optical sensing and condition monitoring.

Keywords:
deep neural networkselectric motor vibrationfiber Bragg grating interrogatorfiber sensing

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Area of Science:

  • Photonics and Sensing Technologies
  • Artificial Intelligence in Engineering
  • Signal Processing and Machine Learning

Background:

  • Fiber Bragg Grating (FBG) sensors are crucial for vibration analysis.
  • Low-power FBG interrogators suffer from low sampling rates, causing undersampling and loss of critical spectral data.
  • Accurate high-frequency vibration recognition is essential for condition monitoring in complex environments.

Purpose of the Study:

  • To develop a novel deep neural network (DNN) framework to overcome the sampling limitations of low-power FBG interrogators.
  • To enable accurate recognition of high-frequency vibrations and closely spaced spectral components from raw time-domain signals.
  • To advance intelligent optical vibration sensing and compact, low-power condition monitoring solutions.

Main Methods:

  • A novel deep neural network (DNN) framework was designed to process raw time-domain signals from FBG sensors.
  • The DNN model was trained to learn and recognize high-frequency and closely spaced vibrational components.
  • The framework was validated using both simulated and experimental datasets.

Main Results:

  • The proposed DNN framework successfully breaks the sampling limit inherent in low-rate FBG interrogators.
  • The model demonstrated superior performance in frequency discrimination across a wide vibrational spectrum.
  • Accurate recognition of high-frequency and extremely close vibrational components was achieved.

Conclusions:

  • The novel DNN framework offers a significant advancement for high-frequency vibration recognition using FBG sensors with low-sampling-rate interrogators.
  • This approach enables effective condition monitoring in complex environments using compact and energy-efficient optical sensing solutions.
  • The study highlights the potential of AI in overcoming hardware limitations in optical sensing applications.