Artificial Intelligence and Machine Learning in Optical Fiber Sensors: A Review

Area of Science:

• Photonics and Sensor Technology
• Artificial Intelligence and Machine Learning

Background:

• Optical Fiber Sensing (OFS) offers robust and versatile sensing capabilities.
• Integration with Artificial Intelligence (AI) promises to significantly enhance OFS performance and adaptability.
• Existing OFS technologies, including localized (FBG, FP, MZI) and distributed (Rayleigh, Brillouin, Raman) systems, can benefit from AI-driven advancements.

Purpose of the Study:

• To provide a comprehensive review of AI-enhanced Optical Fiber Sensing (OFS) technologies.
• To explore the diverse range of AI algorithms applicable to OFS.
• To highlight the transformative applications of AI in optimizing OFS design, operation, and data analysis.

Main Methods:

• Review of localized OFS sensors: Fiber Bragg Gratings (FBG), Fabry-Perot (FP) interferometers, Mach-Zehnder interferometers (MZI).
• Review of distributed OFS systems: Rayleigh, Brillouin, and Raman scattering-based methods.
• Discussion of AI algorithms: Supervised learning, unsupervised learning, reinforcement learning, and deep neural networks.

Main Results:

• AI algorithms are effectively applied to enhance sensor design and optimize interrogation systems.
• AI enables adaptive configuration tuning and advanced interpretation of complex sensor outputs.
• Key applications include denoising, classification, event detection, and failure forecasting in OFS data.

Conclusions:

• The synergy between AI and OFS is creating more intelligent, adaptive, and high-performance sensing solutions.
• AI integration significantly improves the capabilities of both localized and distributed optical fiber sensing.
• Future advancements lie in leveraging AI for more sophisticated data interpretation and predictive maintenance in OFS.