BOTDA Temperature Analysis Based on Artificial Neural Network

In extreme environments, the monitoring of power transmission and transformation equipment has always been a focal point. Distributed sensing technology, due to its distributed characteristics, can be effectively applied in the monitoring of power transmission and transformation equipment. The Brillouin Optical Time Domain Analyzer (BOTDA) based on stimulated Brillouin scattering is widely used in various fields due to its high measurement accuracy, long sensing distance, and ability to measure temperature and strain. How to quickly and accurately extract information from sensing signals is a problem worth studying. This article proposes the application of artificial neural network (ANN) in temperature monitoring and analysis of BOTDA. By analyzing the BOTDA signal, temperature information can be directly extracted from the Brillouin gain spectrum (BGS), and the monitoring performance of ANN under different scanning frequencies is analyzed. The results indicate the reliability of ANN network used for BOTDA signal. This paper provides certain reference significance for using machine learning methods to quickly and accurately extract information from fiber optic sensing signals.