Machine Learning Approaches in Brillouin Distributed Fiber Optic Sensors

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.

Automated summary

This paper presents machine learning approaches applied in the field of Brillouin distributed fiber optic sensors (DFOSs). Brillouin DFOSs have gained popularity due to their ability to continuously monitor temperature and strain along kilometer-long optical fibers, making them attractive for industrial applications. Machine learning has been integrated into Brillouin DFOS signal processing, resulting in enhanced measurements without increasing the system's cost. This paper provides an overview of the applied machine learning methodologies in Brillouin DFOSs and discusses future perspectives in this area.