Recent Advances in Machine Learning for Fiber Optic Sensor Applications

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.

Automated summary

Fiber optic sensors (FOS) have gained significant attention in various industries for monitoring applications, with the potential to become the backbone of intelligent sensing platforms. However, challenges including cross-sensitivity, large data volume, and slow data processing speed need to be addressed. Recent advances in machine learning (ML) and artificial intelligence (AI) offer solutions to overcome these challenges.