Time-Efficient Convolutional Neural Network-Assisted Brillouin Optical Frequency Domain Analysis

Christos Karapanagiotis; Aleksander Wosniok; Konstantin Hicke; Katerina Krebber

doi:10.3390/s21082724

Time-Efficient Convolutional Neural Network-Assisted Brillouin Optical Frequency Domain Analysis

Sensors (Basel). 2021 Apr 13;21(8):2724. doi: 10.3390/s21082724.

Authors

Christos Karapanagiotis¹, Aleksander Wosniok¹, Konstantin Hicke¹, Katerina Krebber¹

Affiliation

¹ Bundesanstalt für Materialforschung und-Prüfung, Unter den Eichen 87, 12205 Berlin, Germany.

Abstract

To our knowledge, this is the first report on a machine-learning-assisted Brillouin optical frequency domain analysis (BOFDA) for time-efficient temperature measurements. We propose a convolutional neural network (CNN)-based signal post-processing method that, compared to the conventional Lorentzian curve fitting approach, facilitates temperature extraction. Due to its robustness against noise, it can enhance the performance of the system. The CNN-assisted BOFDA is expected to shorten the measurement time by more than nine times and open the way for applications, where faster monitoring is essential.

Keywords: Brillouin optical frequency domain analysis; convolutional neural networks; distributed Brillouin sensing; distributed fiber-optic sensors; temperature and strain sensing.