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.

A vibration fiber sensor based on a fiber ring cavity laser and an interferometer based single-mode-multimode-single-mode (SMS) fiber structure is proposed and experimentally demonstrated. The SMS fiber sensor is positioned within the laser cavity, where the ring laser lasing wavelength can be swept to an optimized wavelength using a simple fiber loop design. To obtain a better signal-to-noise ratio, the ring laser lasing wavelength is tuned to the maximum gain region biasing point of the SMS transmission spectrum. A wide range of vibration frequencies from 10 Hz to 400 kHz are experimentally demonstrated. In addition, the proposed highly sensitive vibration sensor system was deployed in a field-test scenario for pipeline acoustic emission monitoring. An SMS fiber sensor is mounted on an 18” diameter pipeline, and vibrations were induced at different locations using a piezoelectric transducer. The proposed method was shown to be capable of real-time pipeline vibration monitoring.

This paper presents a novel probabilistic machine learning (PML) framework to estimate the Brillouin frequency shift (BFS) from both Brillouin gain and phase spectra of a vector Brillouin optical time-domain analysis (VBOTDA). The PML framework is used to predict the Brillouin frequency shift (BFS) along the fiber and to assess its predictive uncertainty. We compare the predictions obtained from the proposed PML model with a conventional curve fitting method and evaluate the BFS uncertainty and data processing time for both methods. The proposed method is demonstrated using two BOTDA systems: (i) a BOTDA system with a 10 km sensing fiber and (ii) a vector BOTDA with a 25 km sensing fiber. The PML framework provides a pathway to enhance the VBOTDA system performance.

This research focuses on enabling real-time monitoring of critical infrastructure including pipelines in oil and gas transportation and the various threats they face, including human error, environmental factors, and material corrosion. The study proposes a novel approach to Structural Health Monitoring (SHM) using ultrasonic in-line pipe inspection with affixed transducers. More specifically, it explores the use of optical fiber sensors in various configurations for continuous monitoring. A significant innovation is the combination of guided wave technology with fiber optic sensors for pipeline and other structural health monitoring applications. The research employs machine learning and artificial intelligence (AI) for corrosion classification through guided waves, using Finite Element Analysis (FEA) simulations to generate labeled datasets for AI training. However, the scarcity of labeled training data in industrial contexts is a challenge. The study also investigates domain adaptation as a tool for knowledge generalization, applying insights from a well-labeled source domain to a less-explored target domain. A novel framework is demonstrated that leverages domain adaptation AI models by integrating simulation data with real-world measurements, thereby enhancing system state estimation and laying the groundwork for future digital twin systems. The thesis presents an innovative approach to infrastructure monitoring by integrating distributed fiber optic sensing, acoustic non-destructive evaluation, and artificial intelligence. This includes corrosion monitoring, reduced-order modeling, integration of AI frameworks with physical-based simulation models, and strategic deployment of optimized acoustic sensing based upon optical fiber sensors.

A novel decoupled radial basis function network (D-RBFN) is proposed to accelerate signal processing and address the big data challenges associated with ultra-long distance Brillouin optical time-domain analysis (BOTDA) systems. The proposed frame- work is demonstrated on a dataset measured over a 100 km distance using a bi-directional Raman assisted BOTDA system.

Reliable defect measurement in industrial pipelines remains difficult under small-sample conditions, where experimental guided-wave data are limited and affected by environmental and instrumentation noise. Although physics-based simulation can provide scalable training data, discrepancies between simulated and measured signals introduce measurement uncertainty and limit the practical reliability of data-driven models. To address this problem, this study presents a small-sample fiber guided-wave defect measurement framework that combines reduced-order modeling, signal adaptation, and domain-aligned classification.In the proposed framework, a reduced-order model derived from high-fidelity wave-propagation simulations is used to efficiently generate physically meaningful guided-wave responses over a broad defect parameter space. To reduce the discrepancy between simulated and experimental data, a neural-style-transfer-based signal adaptation step is introduced to reproduce experimental background characteristics in the time-frequency representation while maintaining the main defect-relevant wave structures. The adapted synthetic signals are then used together with limited experimental measurements to support defect identification in the target domain.The framework is evaluated using experimental guided-wave data collected from two pipeline configurations with repeated measurements at 11 sensor positions. To reduce overlap between training and testing conditions, the target-domain data are partitioned using a position-wise holdout strategy, in which measurements from held-out sensor locations are excluded from training. Results show that the proposed framework achieves the best performance among the compared methods in this held-out setting and improves the practical consistency between simulation-based training data and experimental measurements. These findings suggest that the proposed approach provides a useful route for reducing measurement uncertainty in small-sample fiber guided-wave defect measurement.