TY - JOUR A1 - Karapanagiotis, Christos A1 - Krebber, Katerina T1 - Machine Learning Approaches in Brillouin Distributed Fiber Optic Sensors N2 - This paper presents reported machine learning approaches in the field of Brillouin distributed fiber optic sensors (DFOSs). The increasing popularity of Brillouin DFOSs stems from their capability to continuously monitor temperature and strain along kilometer-long optical fibers, rendering them attractive for industrial applications, such as the structural health monitoring of large civil infrastructures and pipelines. In recent years, machine learning has been integrated into the Brillouin DFOS signal processing, resulting in fast and enhanced temperature, strain, and humidity measurements without increasing the system’s cost. Machine learning has also contributed to enhanced spatial resolution in Brillouin optical time domain analysis (BOTDA) systems and shorter measurement times in Brillouin optical frequency domain analysis (BOFDA) systems. This paper provides an overview of the applied machine learning methodologies in Brillouin DFOSs, as well as future perspectives in this area. KW - Distributed fiber optic sensors KW - BOTDA KW - BOFDA KW - Machine learning KW - Strain and temperature measurements KW - Artificial neural networks PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-580145 DO - https://doi.org/10.3390/s23136187 VL - 23 IS - 13 SP - 1 EP - 26 PB - MDPI AN - OPUS4-58014 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -