A Review of Machine Learning Enabled Distributed Fiber Optic Sensors: Principles and Applications

Abstract

Distributed fiber optic sensors have gained a lot of attention due to their numerous monitoring applications in aerospace, defense, security, civil engineering, and energy monitoring over the last three decades. These examples demonstrate how useful data can be gathered from huge structures with the help of a suitable distributed sensor system for the assessment and management of the monitored structures. Recently, the use of Machine learning (ML), Artificial Intelligence (AI) into the fiber optics for advanced analytics data, rapid data processing, high sensing precision and the ability to categorize the structural events has been demonstrated considerably. The rapid advancement of ML and АТ techniques has revolutionized the field of distributed fiber optic sensors, enabling unprecedented capabilities and applications. This review paper provides a comprehensive analysis of machine learning-enabled distributed fiber optic sensors, focusing on their underlying principles and diverse range of applications. This paper reviews recent developments of ML and AI algorithms integration into distributed fiber optic sensors, discussing the advantages and challenges associated with this combination. Various techniques, such as supervised learning, unsupervised learning, and deep learning, are examined in the context of distributed fiber optic sensing. Moreover, the review addresses the key challenges and limitations associated with ma-chine learning-enabled distributed fiber optic sensors, including data preprocessing, feature ex-traction, model selection, and interpretability of results. Potential solutions and future research directions are also discussed to overcome these challenges and advance the field. This analysis also discusses Potential and Perspective game-changing directions for distributed fiber optical sensor technology development on industrial applications, particularly energy systems monitoring. Possible future outlooks that can be developed with more research have also been mentioned.