Over the past few decades, optical fibers have been widely deployed to implement various applications in high-speed long-distance telecommunication, optical imaging, ultrafast lasers, and optical sensors. Distributed optical fiber sensors characterized by spatially resolved measurements along a single continuous strand of optical fiber have undergone significant improvements in underlying technologies and application scenarios, representing the highest state of the art in optical sensing. This work is focused on a review of three types of distributed optical fiber sensors which are based on Rayleigh, Brillouin, and Raman scattering, and use various demodulation schemes, including optical time-domain reflectometry, optical frequency-domain reflectometry, and related schemes. Recent developments of various distributed optical fiber sensors to provide simultaneous measurements of multiple parameters are analyzed based on their sensing performance, revealing an inherent trade-off between performance parameters such as sensing range, spatial resolution, and sensing resolution. This review highlights the latest progress in distributed optical fiber sensors with an emphasis on energy applications such as energy infrastructure monitoring, power generation system monitoring, oil and gas pipeline monitoring, and geothermal process monitoring. This review aims to clarify challenges and limitations of distributed optical fiber sensors with the goal of providing a pathway to push the limits in distributed optical fiber sensing for practical 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.

Corrosion has been a great concern in the oil and natural gas industry costing billions of dollars annually in the U.S. The ability to monitor corrosion online before structural integrity is compromised can have a significant impact on preventing catastrophic events resulting from corrosion. This article critically reviews conventional corrosion sensors and emerging sensor technologies in terms of sensing principles, sensor designs, advantages, and limitations. Conventional corrosion sensors encompass corrosion coupons, electrical resistance probes, electrochemical sensors, ultrasonic testing sensors, magnetic flux leakage sensors, electromagnetic sensors, and in-line inspection tools. Emerging sensor technologies highlight optical fiber sensors (point, quasi-distributed, distributed) and passive wireless sensors such as passive radio-frequency identification sensors and surface acoustic wave sensors. Emerging sensors show great potential in continuous real-time in-situ monitoring of oil and natural gas infrastructure. Distributed chemical sensing is emphasized based on recent studies as a promising method to detect early corrosion onset and monitor corrosive environments for corrosion mitigation management. Additionally, challenges are discussed including durability and stability in extreme and harsh conditions such as high temperature high pressure in subsurface wellbores.

Timely detection of the incipient fault of a power transformer is of utmost importance to prevent potential malfunctioning. Monitoring the condition of the transformer insulation oil is the essential step as it contains most of the transformer’s health diagnostic information. Thus, the temperature of the oil is considered the most crucial parameter that has to be monitored continuously in real-time. Distributed optical fiber sensors for temperature monitoring has various advantages over traditional methods. In this study, an optical fiber-based distributed temperature measurement of insulation oil of a fully energized 100 kVA commercial distribution transformer is demonstrated. Two types of techniques were deployed to monitor the temperature of the transformer inside and outside of the tank using a distributed Optical Frequency Domain Reflectometry (OFDR) and a quasi-distributed Fiber Bragg Grating (FBG) sensor array respectively. The insulation oil temperature was monitored with different load conditions in a distributed fashion and compared with the conventional method of single point thermocouple and infrared thermal imaging. The test results show very good agreement between the conventional methods and proposed distributed fiber temperature 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.

This article investigates using optical fibers with enhanced backscattering profiles to improve distributed fiber sensor performance and reduce instrumentation costs. Using a femtosecond (fs) laser direct writing technique, the Rayleigh backscattering profile of a standard telecom fiber was enhanced by more than 40 dB to improve the signal-to-noise ratio (SNR) for optical frequency domain reflectometry (OFDR). The enhanced backscattering signals enable effectively distributed strain measurements using a low-cost tunable laser (TL). Median filtering is applied to denoise cross correlation results to further improve measurement outcomes. Results presented in this article show that a TL with a 1-nm tuning range, which is far less than the tuning range used in commercial OFDR interrogators, can perform effectively distributed strain measurements using sensing fibers with enhanced backscattering profiles. The sensing fiber with over 40-dB backscattering enhancement achieved 4.8-cm spatial resolution in strain measurements with a root mean square accuracy of less than 2.70 $\mu \varepsilon $ when 10–50 $\mu \varepsilon $ were exerted to the sensing fiber. Results presented in this article reveal both the potential and limitations of sensing fibers with enhanced backscattering for OFDR-based distributed fiber sensors.