Recent Advances in Machine Learning for Fiber Optic Sensor Applications

TLDR

Fiber‑optic sensors are increasingly used across aerospace, defense, civil engineering, and energy, yet their adoption is limited by cross‑sensitivity, massive data generation, slow processing, signal‑to‑noise degradation over distance, and high system cost. This review surveys recent work that couples machine‑learning and artificial‑intelligence algorithms with fiber‑optic sensing, identifies promising technology directions—especially for energy‑system monitoring—and outlines future research priorities. The authors compile and analyze studies integrating ML/AI techniques into fiber‑optic sensor platforms, evaluating their impact on performance and application potential.

Abstract

Over the last three decades, fiber optic sensors (FOS) have gained a lot of attention for their wide range of monitoring applications across many industries, including aerospace, defense, security, civil engineering, and energy. FOS technologies hold great promise to form the backbone for next‐generation intelligent sensing platforms that offer long‐distance, high‐accuracy, distributed measurement capabilities and multiparametric monitoring with resilience to harsh environmental conditions. The major limitations posed by FOS are 1) cross‐sensitivity, 2) enormous volume and large data generation, 3) low data processing speed, 4) degradation of signal‐to‐noise ratio over the fiber length, and 5) overall cost of sensor and interrogator systems. These challenges can be overcome by building advanced data analytics engines enabled by recent breakthroughs in machine learning (ML) and artificial intelligence (AI). This article presents a comprehensive review of recent studies that integrate ML and AI algorithms with FOS technologies. This review also highlights several FOS technology development directions that promise a significant impact on widespread use for several industrial applications, with an emphasis on energy systems monitoring. A perspective on future directions for further research development is also provided.