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Simultaneous Temperature and Relative Humidity Measurement Using Machine Learning in Rayleigh-Based Optical Frequency

Mateusz Mądry1, Bogusław Szczupak1, Mateusz Śmigielski1

  • 1Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland.

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This study introduces a machine learning (ML) model for simultaneous temperature and relative humidity (RH) measurement using Rayleigh-based Optical Frequency Domain Reflectometry (OFDR). This ML approach enhances accuracy and reduces analysis time for environmental sensing applications.

Keywords:
distributed fiber sensorshumidity sensormachine learningoptical fiber sensorstemperature sensor

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Area of Science:

  • Optical sensing technologies
  • Machine learning applications in metrology
  • Environmental monitoring instrumentation

Background:

  • Accurate simultaneous measurement of temperature and relative humidity (RH) is crucial for various applications.
  • Traditional methods often require complex post-processing or separate sensors.
  • Rayleigh-based Optical Frequency Domain Reflectometry (OFDR) offers a promising platform for distributed sensing.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model for simultaneous temperature and RH sensing using OFDR.
  • To investigate the performance of a dual-segment fiber optic sensor (bare and polyimide-coated) for differential environmental sensitivity.
  • To eliminate manual data post-processing and reduce analysis time in OFDR-based measurements.

Main Methods:

  • Utilized a sensor unit comprising bare and polyimide-coated fiber segments with distinct temperature sensitivities.
  • Developed a machine learning (ML) model to process OFDR data for simultaneous temperature and RH extraction.
  • Evaluated the impact of sensor unit length and data point count on measurement accuracy (RMSE).

Main Results:

  • Achieved high accuracy for simultaneous temperature and RH measurements with Root Mean Square Error (RMSE) of 0.36 °C and 1.73% RH, respectively, for a 3 cm sensor length.
  • Demonstrated the effectiveness of the ML model in distinguishing and quantifying temperature and RH contributions.
  • Quantified the influence of sensor configuration parameters on overall measurement precision.

Conclusions:

  • The proposed ML-driven OFDR system enables accurate, simultaneous, and automated measurement of temperature and RH.
  • This approach significantly reduces data analysis time compared to traditional post-processing methods.
  • The developed technique offers a robust solution for environmental sensing in diverse fields requiring precise monitoring.