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

Lidan Cao1, Sabrina Abedin1, Guoqiang Cui1

  • 1Electrical and Computer Engineering Department, University of Massachusetts Lowell, Lowell, MA 01854, USA.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances optical fiber sensing (OFS) for smarter systems. This review covers AI algorithms and applications in localized and distributed OFS for improved performance and data interpretation.

Keywords:
artificial intelligencemachine learningneural networksoptical fiber sensors

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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.