Machine Learning Assisted Fiber Bragg Grating-Based Temperature Sensing

Journal article


Publication Details

Author(s): Djurhuus MSE, Werzinger S, Schmauß B, Clausen AT, Zibar D
Journal: IEEE Photonics Technology Letters
Publication year: 2019
Volume: 31
Journal issue: 12
Pages range: 939-942
ISSN: 1041-1135


Abstract

This letter proposes an alternative approach to the signal processing of temperature measurements based on fiber Bragg gratings (FBGs) using the machine learning tool Gaussian process regression (GPR). The 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. The GPR also has the potential to improve the measurement speed of FBG-based temperature sensing compared to current standards.


FAU Authors / FAU Editors

Schmauß, Bernhard Prof. Dr.-Ing.
Professur für Hochfrequenztechnik mit dem Schwerpunkt Optische Hochfrequenztechnik und Photonik
Werzinger, Stefan
Lehrstuhl für Hochfrequenztechnik


External institutions with authors

Technical University of Denmark / Danmarks Tekniske Universitet (DTU)


How to cite

APA:
Djurhuus, M.S.E., Werzinger, S., Schmauß, B., Clausen, A.T., & Zibar, D. (2019). Machine Learning Assisted Fiber Bragg Grating-Based Temperature Sensing. IEEE Photonics Technology Letters, 31(12), 939-942. https://dx.doi.org/10.1109/LPT.2019.2913992

MLA:
Djurhuus, Martin S. E., et al. "Machine Learning Assisted Fiber Bragg Grating-Based Temperature Sensing." IEEE Photonics Technology Letters 31.12 (2019): 939-942.

BibTeX: 

Last updated on 2019-18-06 at 18:08