Machine learning assisted Fiber Bragg Grating based temperature sensing

Martin Søren Engmann Djurhuus*, Stefan Werzinger, Anders Clausen, Darko Zibar

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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This paper proposes an alternative approach to the signal processing of temperature measurements based on Fiber Bragg Gratings (FBG) using the machine learning tool Gaussian Process Regression (GPR). Experimental results show that for a majority of the cases under consideration the reported technique provides a more accurate calculation of the temperature than the conventional methods. Furthermore, the GPR can give the uncertainty of an estimate together with the estimate itself, which for example is useful when it is important to know the worst case scenario of a measurand. GPR also has the potential to improve the measurement speed of FBG based temperature sensing compared to current standards.
Original languageEnglish
JournalIEEE Photonics Technology Letters
Issue number12
Pages (from-to)939-942
Publication statusPublished - 2019


  • Machine learning
  • Gaussian processes
  • Optical fiber sensors
  • Bragg gratings

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