Machine Learning Approaches in Brillouin Distributed Fiber Optic Sensors

Area of Science:

• Optoelectronics and Sensor Technology
• Machine Learning Applications
• Fiber Optic Sensing

Background:

• Brillouin distributed fiber optic sensors (DFOSs) offer continuous, long-range temperature and strain monitoring.
• Industrial applications include structural health monitoring of infrastructure and pipelines.
• Traditional signal processing limits measurement speed and resolution.

Purpose of the Study:

• To review machine learning (ML) methodologies applied to Brillouin DFOSs.
• To highlight ML's impact on measurement accuracy, speed, and resolution.
• To discuss future research directions in this interdisciplinary field.

Main Methods:

• Literature review of ML algorithms used in Brillouin DFOS signal processing.
• Analysis of ML's role in enhancing Brillouin optical time domain analysis (BOTDA) and Brillouin optical frequency domain analysis (BOFDA).
• Categorization of ML techniques based on their application in DFOS data analysis.

Main Results:

• ML integration leads to faster and more accurate temperature, strain, and humidity measurements.
• ML improves spatial resolution in BOTDA systems.
• ML significantly reduces measurement times in BOFDA systems without added cost.
• ML enables enhanced data analysis for complex sensing scenarios.

Conclusions:

• Machine learning is a transformative technology for Brillouin distributed fiber optic sensing.
• ML offers cost-effective improvements in performance for critical industrial monitoring.
• Future research should focus on advanced ML models for even greater sensing capabilities.