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Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor

Yibeltal Chanie Manie1, Jyun-Wei Li1, Peng-Chun Peng1

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This summary is machine-generated.

A new method uses a long short-term memory (LSTM) machine learning algorithm with data de-noising to accurately measure strain in fiber Bragg grating (FBG) sensor networks. This approach improves signal accuracy in noisy environments.

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Area of Science:

  • Optical sensing technologies
  • Machine learning applications in engineering
  • Signal processing for sensor networks

Background:

  • Intensity wavelength division multiplexing (IWDM)-based fiber Bragg grating (FBG) sensor networks are crucial for strain sensing.
  • Unstable output power and environmental noise can degrade FBG spectral data, hindering accurate strain measurement.
  • Effective noise reduction and signal processing are vital for enhancing FBG sensor network performance.

Purpose of the Study:

  • To propose an effective strain sensing signal measurement method for IWDM-based FBG sensor networks.
  • To integrate a long short-term memory (LSTM) machine learning algorithm with data de-noising techniques.
  • To improve the accuracy and robustness of strain sensing in challenging environments.

Main Methods:

  • Utilized a distributed IWDM-based FBG sensor network with an optical coupler featuring distinct output power ratios (70%, 60%, 40%, 30%).
  • Employed discrete waveform transform (DWT) for data de-noising to reduce noise and improve signal quality.
  • Applied a long short-term memory (LSTM) deep learning model to process de-noised data for accurate FBG sensing signal measurement.

Main Results:

  • The integration of LSTM with DWT data de-noising significantly enhanced sensing signal measurement accuracy.
  • The proposed method demonstrated robust performance even in noisy data or harsh environmental conditions.
  • Experimental results validated the improved signal-to-noise ratio and accurate measurement of FBG sensing signals.

Conclusions:

  • The proposed LSTM and DWT integrated method offers accurate strain sensing in IWDM-based FBG networks, even in noisy environments.
  • This approach enhances the capacity and multiplexing capability of FBG sensor systems.
  • The method provides a reliable solution for advanced strain monitoring applications.