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.

The potential to use single-crystal sapphire optical fiber as an alternative to silica optical fibers for sensing in high-temperature, high-pressure, and chemically aggressive harsh environments has been recognized for several decades. A key technological barrier to the widespread deployment of harsh environment sensors constructed with sapphire optical fibers has been the lack of an optical cladding that is durable under these conditions. However, researchers have not yet succeeded in incorporating a high-temperature cladding process into the typical fabrication process for single-crystal sapphire fibers, which generally involves seed-initiated fiber growth from the molten oxide state. While a number of advances in fabrication of a cladding after fiber-growth have been made over the last four decades, none have successfully transitioned to a commercial manufacturing process. This paper reviews the various strategies and techniques for fabricating an optically clad sapphire fiber which have been proposed and explored in published research. The limitations of current approaches and future prospects for sapphire fiber cladding are discussed, including fabrication methods and materials. The aim is to provide an understanding of the past research into optical cladding of sapphire fibers and to assess possible material systems for future research on this challenging problem for harsh environment sensors.

Real-time temperature mapping that solves local overheating problems is important for obtaining an optimized thermal design for high-efficiency power transformers. Internal temperature monitoring of operating power transformers can also be leveraged for asset monitoring applications targeting at fault detection enabling condition-based maintenance. Transformers present a variety of challenging sensing environments such as high-levels of electromagnetic interference and limited space for conventional sensors to operate. Immersion of power transformers in insulation oils for thermal management during operation and the presence of relatively large and time varying electrical and occasional magnetic fields make sensing technologies requiring electrical wires or active power at sensing locations highly undesirable. In this work, we investigate thermal response of a standard telecom fiber instrumented on compact transformer cores by using an optical frequency-domain reflectometry scheme. Correlation between conventional temperature sensing methods and fiber-optic sensing results as well as trade-offs between spatial resolution and temperature measurement accuracy is discussed and spatially resolved real-time monitoring of temperatures in energized transformers is experimentally demonstrated.

Modern high-temperature processes such as fossil energy production, nuclear reactors, and chemical reactors lack robust, distributed sensing systems to map temperatures in these high-value harsh-environment systems. Regular silica-fiber-based distributed temperature sensing (DTS) systems usually only operate at temperatures below about 800 °C. In this paper, we present the design, implementation, and testing of a distributed ultra-high temperature sensing system using Raman scattering intensity, which operates from room temperature to above 1400 °C. Consideration is given to the impacts of thermal radiation, fluorescence, and the multimode nature of unclad single-crystal fiber to optimize the system. Results from picosecond and sub-nanosecond lasers were compared. Measurements were taken with a ~2 m sapphire optical fiber; which represents the longest commercially available length. A spatial resolution of 12.4 cm and position standard deviation of 0.28 mm were achieved up to the maximum testing temperature of 1400 °C, which is a new record for distributed temperature sensing systems.

One of the major challenges for metal-powder-based additive manufacturing is measuring and mitigating residual strain induced during the manufacturing processes. This paper reports distributed fiber optic sensors embedded in Inconel alloy components as experimental means to validate numerical models of additive manufacturing process. Electroplating was used to deposit a metal protective jacket onto standard telecom single-mode fibers for strain measurements, Fiber sensors were embedded in an Inconel alloy substrate using the Laser Engineered Net Shaping (LENS) process. Using a Rayleigh-scattering optical frequency domain reflectometer (OFDR), temperature changes and residual strain in the metal substrate were monitored with 5 mm spatial resolution during the LENS process. Using finite element analysis, temperature and strain profiles induced by the LENS deposition processes were also numerically studied. Discrepancies between the simulated temperature and strain profiles and those measured directly were less than 10%. Results presented in this paper demonstrates a digital twin approach to fuse modeling results with distributed fiber sensor measurement data to study additive manufacturing process toward design and fabrication process optimization.

A new hardware configuration to correct nonlinear tuning affects in optical frequency domain reflectometry (OFDR) is proposed. Unlike conventional correction methods, the new configuration does not require a separate auxiliary interferometer detection, which may eliminate the need for an extra detection device and an acquisition channel. In the proposed method, an intentional beat signal is introduced at the beginning of the OFDR spectrum, which is treated as an auxiliary interferometer to acquire tunable laser phase information for the nonlinear tuning correction. The proposed method may reduce the overall system cost, reduce the data acquisition time and computational load, and make system configuration simpler by eliminating the need for extra components. In this paper, feasibility of the proposed configuration is demonstrated by correcting nonlinear tuning affects using simulations and experiments in a ~ 50 m long optical fiber. Distributed strain and temperature sensing are also performed in a 50 m optical fiber with the proposed new configuration.