Fiber Optic Sensor Research Articles

Sapphire optical fiber with (BN/SiBCN)n coating prepared by chemical vapor deposition for high-temperature sensing applications

To enhance the load-bearing capacity of sapphire optical fiber (SOF) in extreme environments, this study prepared SOF/(BN/SiBCN)n by optimizing the nanostructure through increased deposition temperature of BN using alternating chemical vapor deposition. Simulation results indicated an inverse correlation between the leakage loss of SOF/BN/SiBCN and BN thickness; at room temperature, the tendency for coating cracking along the radial direction of SOF/BN/SiBCN is positively correlated with BN thickness, whereas this trend reverses at 1500 °C. Mechanical testing revealed that the tensile strength of SOF/BN/SiBCN is inversely correlated with the tendency for interlayer cracking, aligning with the findings from residual stress simulations; under an air environment at 1500 °C, SOF/(BN/SiBCN)2 exhibited the highest tensile strength, indicating that the layered matrix design effectively mitigates the risk of BN oxidation by creating barrier layers. Optical test demonstrated that the BN coating functions as a total reflection layer to suppress the scattering of optical signals on the sapphire optical fiber surface. In conclusion, the coating design of SOF/(BN/SiBCN)n could meet the material requirements for sapphire optical fiber sensors in extreme environments.

Carbon-Nanotube-Based Optical Fiber Sensor With Rapid Response for Human Breath Monitoring and Voiceprint Recognition

Carbon-Nanotube-Based Optical Fiber Sensor With Rapid Response for Human Breath Monitoring and Voiceprint Recognition

Modeling of High-Performance Fiber Optic SPR Sensor for Colorectal Cancer Detection Designed Using Amorphous Silicon and TiO2 Layers Under Optimum Radiation Damping

Modeling of High-Performance Fiber Optic SPR Sensor for Colorectal Cancer Detection Designed Using Amorphous Silicon and TiO₂ Layers Under Optimum Radiation Damping

Signal recognition based on transfer learning for Φ-OTDR fiber optic distributed disturbance sensor

Abstract Traditional classification models for optical fiber vibration signals can achieve good recognition results when there are a large number of samples. However, it is difficult to obtain sufficient training samples in real scenes. A method of Φ-OTDR sensor signal recognition based on VGGish transfer learning is proposed in this paper. Firstly, a selection indicator is designed to filter out vibration signals from sparse signals. Then, the VGGish network pretrained for sound classification is used for transfer learning as a feature extractor for optical fiber vibration signals. This method was applied to classify 6 kinds of optical fiber vibration signals. When the number of training samples is reduced to 480 samples, the method can still achieve 84.17% classification accuracy. Compared to the method of training from scratch, this proposed method reduces training time by 73.9%.

Fiber Optic Wearable Curvature Sensor Based on Taper-Assisted Microcavity

Fiber Optic Wearable Curvature Sensor Based on Taper-Assisted Microcavity

Optical Fiber Bragg Grating As A Temperature Sensor

The optical fibre sensor (OFS) has become quite well-known and prominent in the sensor technology industry. It is often used to react to outputs from other systems, like those in industrial, monitoring, and chemical analysis applications, and to detect changes in the environment. A Fibre Bragg Grating (FBG) is a device that allows light to be reflected from a short section of optical fiber at a specific wavelength, while the Bragg reflector expands and transmits all other wavelengths. The current effort focuses on the evolving characteristics and behaviors of strain and temperature sensors operating on fiber bragg gratings through computer modeling. The temperature sensor is employed to detect and quantify any variations in temperature on the Fiber Bragg Grating that could result in accidents or fires. This will show how the Fibre Bragg Grating may be used to showcase temperature sensors.

Sub-surface thermal measurement in additive manufacturing via machine learning-enabled high-resolution fiber optic sensing

Microstructures of additively manufactured metal parts are crucial since they determine the mechanical properties. The evolution of the microstructures during layer-wise printing is complex due to continuous re-melting and reheating effects. The current approach to studying this phenomenon relies on time-consuming numerical models such as finite element analysis due to the lack of effective sub-surface temperature measurement techniques. Attributed to the miniature footprint, chirped-fiber Bragg grating, a unique type of fiber optical sensor, has great potential to achieve this goal. However, using the traditional demodulation methods, its spatial resolution is limited to the millimeter level. In addition, embedding it during laser additive manufacturing is challenging since the sensor is fragile. This paper implements a machine learning-assisted approach to demodulate the optical signal to thermal distribution and significantly improve spatial resolution to 28.8 µm from the original millimeter level. A sensor embedding technique is also developed to minimize damage to the sensor and part while ensuring close contact. The case study demonstrates the excellent performance of the proposed sensor in measuring sharp thermal gradients and fast cooling rates during the laser powder bed fusion. The developed sensor has a promising potential to study the fundamental physics of metal additive manufacturing processes.

Simultaneous measurement of the distributed longitudinal strain and velocity field for a cantilevered cylinder exposed to turbulent cross flow

The structural response of a cantilevered cylinder under free-stream conditions both with low and high turbulence intensity generated by turbulence-generating grids is analysed with concurrent measurements of the velocity field and the distributed strain. The thin-walled cylinder, with a diameter (D) of 50 mm, is mounted in a water flume as a cantilevered beam supported at one end, with 95% of its body submerged and exposed to cross flow yielding a Reynolds number of Re = 25000. We employ a novel combination of simultaneous particle image velocimetry and distributed strain measurements using Rayleigh backscattering fibre optic sensors. These sensors are embedded onto the surface of the cylinder to measure the experienced strain (ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon $$\\end{document}) of the structure along its spanwise direction, covering both the windward and leeward faces of the cylinder. The sensors fine spatial resolution allows us to discern the influence of the flow on the structural response of the cylinder in two distinct regions of the structure: upstream and downstream of the mean separation location. This differentiation allows us to isolate the local effects introduced by the free-stream conditions on the loading events over the body from the global force generated by the vortex shedding and other coherent motions present within the flow. Distinguishing between these direct and indirect effects helps determine which is more relevant for fatigue-life cycle analysis. The cross power spectral density between the fluctuating velocity field and the strain reveals that the load is dominated by the vortex shedding, and this relationship is intensified with the introduction of free-stream turbulence. It also helps to discern the different dynamics imposed by the two free-stream turbulence conditions.

Achieving precise multiparameter measurements with distributed optical fiber sensor using wavelength diversity and deep neural networks

The development of advanced distributed optical fiber sensing systems that are capable of performing accurate and spatially resolved multiparameter measurements is of great interest to a wide range of scientific and industrial applications. Here, we propose and experimentally demonstrate a wavelength diversity based advanced distributed optical fiber sensor system to accomplish multiparameter sensing while greatly enhancing measurement accuracy. A suite of deep neural network (DNN) algorithms are developed and verified for data denoising, rapid Brillouin frequency shift estimation, and vibration data event classification. As a proof-of-concept, we demonstrate the effectiveness of the proposed advanced wavelength diversity distributed fiber sensor system assisted by DNN for simultaneous, independent measurements of static strain, temperature, and acoustic vibrations over a 25 km long sensing fiber at 3 m spatial resolution. These results suggest the potential for an intelligent multiparameter monitoring system with enhanced performance in advanced structural health monitoring applications.

Polymer enhanced sensitivity and figure of merit SPR optical fiber sensor for high refractive index and wide detection range

A highly sensitive sensor based on surface plasmon resonance (SPR) for high refractive index (RI) sensing is proposed, whose most unique feature lies in a high RI of polyvinylpyrrolidone (PVP) layer embedded between the optical fiber and Au film. The use of the high RI PVP layer amplifies the evanescent wave, thereby increasing sensitivity and improving figure of merit (FOM) of the SPR sensor. In addition, SPR sensor coated with a high RI PVP film breaks through the limitation that the inherent RI of optical fiber can only detect low RI, and can measure higher RI values and expand the measurement range. Experimental results demonstrate that, with a PVP layer thickness of 60 nm, showcasing superior performance compared to sensors lacking a PVP layer. Notably, this SPR sensor offers a cost-effective and straightforward fabrication process, promising broad application in environmental monitoring and biosensing.

Optical Fiber Bragg Grating as a Strain Sensor

Abstract: The sensor technology industry has come to recognize and value the optical fibre sensor (OFS). It is frequently used to detect changes in the environment and to respond to outputs from other systems, such as those in industrial, monitoring, and chemical analysis applications. While a brief segment of optical fiber with a particular wavelength is reflected with light in a device known as a Fibre Bragg Grating, a Bragg reflector begins to grow and transmit all other wavelengths. Through software modeling, the current study focuses on the changing traits and behaviors of temperature and strain sensors that are used with fiber bragg gratings (FBG). The main objective of this work is to study the properties and behavior of strain sensors utilized in conjunction with fiber bragg gratings. A strain sensor can be used to determine the strain in an object whose resistance changes when force is applied

Identifying mode coupling wavelengths in doubly-clad optical fibers with deep learning

Understanding the transmission of light waves in optical fibers and accurately determining the locations of mode coupling are crucial for enhancing the efficiency of optical devices and advancing innovative technologies such as fiber optic sensors, lasers, and modulators. This study utilizes deep learning and image recognition techniques to identify the wavelengths at which mode coupling occurs in optical fibers. Our research findings show that using the ResNet-18 model allows for the rapid and accurate identification of the wavelengths at which mode coupling occurs in optical fibers, as well as the modes involved, achieving an accuracy close to 100 %. We experimented with sampling the dataset at 5 nm and 10 nm intervals to create smaller training and validation sets. Despite the reduced data volume, high accuracy rates were maintained, exceeding 99 % and 97 % respectively. This study provides new insights into the use of deep learning for precise localization of mode coupling points and tracking of transmission modes in optical fibers.

A rapid response optical fiber sensor with highly selective probe for trace detection of mercury (II) ions

A highly sensitive, selective, and well-repeatable optical fiber sensor was proposed for trace measurement of Hg2+ concentration in the water environment based on Mach-Zehnder interference. The proposed structure was prepared by splicing a piece of thin core fiber (TCF) between multimode fibers (MMF) to form MMF-TCF-MMF. A novel sensitive film 3-mercaptopropyl-trimethoxysilane (MPTMS) modified polyvinyl alcohol (PVA) has been designed and synthesized, and uniformly coated on the surface of TCF. The specific combination of Hg2+ with PVA-MPTMS sensitive film can change the effective refractive index of the coating mode, enabling the measurement of Hg2+ concentration by tracking the interference spectra. The results show that the sensitivity of the PVA-MPTMS-coated sensor can achieve 17.5 nm/μM with a detection limit of 3.42 nM in the concentration range of 0–1000 nM. The sensor represents excellent repeatability and stability and a rapid response time of roughly 90 s. Meanwhile, the proposed sensor shows a specific recognition of Hg2+ and is unaffected by many other interfering metal ions in the water, which corresponds well with the binding energy calculated by the density functional theory (DFT). The sensor for Hg2+ detection exhibits compact structure, rapid response, high selectivity and repeatability, showing promising potential applications in the field of environmental monitoring.

A Simultaneous Measurement Sensor for Temperature and Curvature Based on Congruent Quasi-Helical Long-Period Fiber Grating.

This article presents a long-period fiber-grating sensor based on a congruent quasi-helical structure (CQH-LPFG) with the two-parameter measurement of both temperature and curvature. The CQH-LPFG sensor was manufactured using a high-frequency CO2 laser, and an innovative quasi-helical structure was introduced into the two-parameter measurement of the temperature and curvature of the optical fiber sensor with excellent results. The experiment and analysis demonstrate that the curvature sensitivities of the three resonance peaks in the 1440 nm to 1540 nm transmission spectrum were 11.88 nm/m-1, 8.05 nm/m-1, and 11.11 nm/m-1, and the curvature varied ranging from 0.156 m-1 to 0.494 m-1. The three resonance peaks showed temperature responsivities of 29.87 pm/°C, 24.65 pm/°C, and 36.85 pm/°C, respectively, and the linear fit was of excellent quality. In the case of measuring both curvature and temperature changes simultaneously, the resonant peak wavelength of the CQH-LPFG sensor was demodulated through matrix analysis, with dip A and dip C providing superior simultaneous measurements. These features make it a promising candidate for applications such as engineering machinery and the health inspection of buildings.

Distributed fiber optic sensing (DFOS) as a part of diagnostics and monitoring of prestressed structures

Distributed Fiber Optic Sensing (DFOS) is a measurement technology that has experienced dynamic development in recent years and is increasingly used in various industries, including construction. By embedding sensors during the manufacturing stage of prestressed concrete elements, significant insights can be gained into their behavior during concrete pouring, prestressing, curing, and under rheological influences. DFOS sensors allow for the analysis of structural behavior under load and changes occurring over time with a level of accuracy previously unattainable with conventional methods. The paper presents research results on the implementation of this technology in pre-tensioned and post-tensioned elements. The main objective of the research was to verify the feasibility of using DFOS sensors to identify damages (e.g., cracks and simulated voids in cable ducts) and monitor the state of deformations and displacements in prestressed concrete elements.

A Fast and Accurate Mapping Method for an OPGW Tower Based on Hybrid Distributed Optical Fiber Sensing.

The combination of the dark fiber in existing Optical Fiber Composite Overhead Ground Wire (OPGW) with Distributed Optical Fiber Sensing (DOFS) technology can be used to enable online monitoring and provide early warnings of anomalies in high-voltage transmission lines. Accurate mapping of the optical cable length to the geographic coordinates of actual towers is a key factor in achieving this goal. This paper discusses the principle of using a DOFS system for transmission line tower positioning and presents four available positioning features. To overcome the limitations of single physical parameter positioning, this paper presents a self-developed hybrid DOFS that simultaneously captures Rayleigh backscattering and Brillouin scattering signals. Several physical parameters, including temperature, strain, and vibration, are acquired synchronously. Through hybrid multi-parameter analysis, the rapid and accurate positioning of OPGW line towers is achieved. Experimental results have shown that the proposed method, based on the hybrid DOFS system, can locate up to 82 towers, while the traditional method could only identify 12. The hybrid system was able to complete 80% of the tension towers in 40 h. This paper presents a novel multi-parameter localization method that has the potential to significantly improve the efficiency and reliability of grid operation and maintenance.

Development of a theory-monitoring integrated structure joints assessment method of prefabricated underground stations

Prefabricated assembly structures have been extensively applied for the construction of low carbon and cost effective underground subway stations in China. Engineers faced significant challenges in dealing with the assessment of mechanical performance of structures joints within prefabricated underground station (PUS) structures during construction process. Manual assembly of large-scale prefabricated structures may lead to significant disturbance of structures joints, resulting in structures joints failure in construction process or even water leakage in maintenance of underground stations. To address these issues, this study proposed an integrated theory-monitoring method for joint performance assessment. The initial step involved the establishment of a fiber optical sensors (FOS) monitoring system for PUS joints. A joint performance assessment module was then developed utilizing the Joint Interfaces Contact Model (JICM) and was subsequently integrated into monitoring system, forming a joint performance assessment method, namely FOS-JICM (FOJI). A case study was finally conducted on a Shenzhen pipe jacking PUS to evaluate the effectiveness of FOJI. Main research findings of this study include (a) FOS monitoring system demonstrated excellent adaptability, accuracy, and real-time performance in practical applications; (b) FOS monitoring system effectively captured joint deformation and stress evolution throughout the entire construction process, providing insights into the impact of complex geological conditions and box culverts on joint behavior; (c) FOJI, with its integration of the JICM module, enabled safety assessment of joint performance during pipe jacking process; (d) FOJI successfully identified joint damage, offering valuable guidance for engineering restoration and protective measures. It facilitated prompt updates and adjustments based on the actual construction conditions, establishing a cycle mechanism for safety protection. These achievements not only establish a theoretical foundation for ensuring the construction safety of PUS joints but also present substantial practical application value.

Diameter Optimization of No-Core Fiber for High Refractive Index Sensing

This study presents a simulation-based modal analysis to optimize the no-core fiber (NCF) sensor design for high refractive index (HRI) applications. The goal is to improve the sensitivity of the sensor towards HRI by solving problems in conventional optical fiber sensors (OFS), such as sophisticated fabrication methods, high fragility, and potential effects on long-term reliability. Furthermore, in previous work, most OFS applications focused on detecting mediums with a low refractive index (LRI). Hence, the goal of this study is to overcome these constraints. For this research, the Wave Optics module in COMSOL Multiphysics® software was used to optimize the NCF diameter, which ranged from 100 μm to 150 μm. Simulations include changes in the refractive index (RI) of the analyte in the surrounding medium of the sensor to analyse the efficiency of the NCF in HRI sensing completely, ranging from 1.46 RIU to 1.56 RIU. The performance is then assessed by monitoring the intensity change in the power spectrum, which reflects the reaction of leaky modes to various HRI conditions. The findings indicate that decreasing the size of the NCF sensor enhances sensitivity in detecting HRI mediums by amplifying the leakage loss of each order leaky mode, as opposed to a larger NCF diameter. This effect is due to the reduced confinement of modes in a smaller diameter NCF. The 100 μm diameter NCF has exceptional refractive index (RI) sensitivity and linearity, measuring 88.996 dB/RIU and 0.7783, respectively. Accordingly, NCF proves to be a promising candidate for utilization in HRI sensing applications, given its high sensitivity and straightforward fabrication, making it easily implementable and practical for real-world use.

Real-time in situ phase sensitivity calibration of interferometric fiber-optic ultrasonic sensors.

Output from a fiber-optic interferometric ultrasonic sensor can be affected by the operating point changes and/or the optical power fluctuations. We report a method for real-time and in situ calibration of the responses to ultrasound-induced phase delays for a low-finesse Fabry-Perot (FP) ultrasonic sensor demodulated using a phase-generated carrier. The carrier is produced from an electro-optic phase modulator, and its zero- and fundamental-carrier frequency components yield two quadrature signals. The same phase modulator is employed to generate controllable laser frequency modulations and calibration phase delay modulations. The response to the known calibration phase delays is used to gauge the sensor output in real time and in situ. The experiment has verified the effectiveness of the calibration method against the signal variations from both operating point changes and optical power variations.

FiberFlex: Real-time FPGA-based Intelligent & Distributed Fiber Sensor System for Pedestrian Recognition

In recent years, security monitoring of public places and critical infrastructure has heavily relied on the widespread use of cameras, raising concerns about personal privacy violations. To balance the need for effective security monitoring with the protection of personal privacy, we explore the potential of optical fiber sensors for this application. This paper proposes FiberFlex, an intelligent and distributed fiber sensor system. Ultizing FPGA high-level synthesis (HLS) acceleration, FiberFlex offers real-time pedestrian detection by co-designing the entire pipeline of optical signal acquisition, processing, and recognition networks based on the principles of optical fiber sensing. As a promising alternative to traditional camera-based monitoring systems, FiberFlex achieves pedestrian detection by analyzing the vibration patterns caused by pedestrian footsteps, enabling security monitoring while preserving individual privacy. FiberFlex comprises three modules: First , fiber-optic sensing system: A fiber-optic distributed acoustic sensing (DAS) system is built and used to measure the ground vibration waves generated by people walking. Second , algorithms: We first collect the training data by measuring the ground vibration waves, label the data, and use the data to train the neural network models to perform pedestrian recognition. Third , hardware accelerators: We use HLS tools to design hardware modules on FPGA for data collection and pre-processing, and integrate them with the downstream neural network accelerators to perform in-line real-time pedestrian detection. The final detection results are sent back from FPGA to the host CPU. We implement our system FiberFlex with the in-house built DAS system and AMD/Xilinx Kintex7 FPGA KC705 board and verify the whole system using the real-world collected data. We conduct recognition tests on 5 test subjects of varying ages, heights, and weights in a fixed sensing area. Each subject experienced 20 real-time recognition tests using their daily walking habits, and the subjects were given adequate rest between tests. After 100 tests on five test subjects, the overall real-time recognition accuracy exceeded \(88.0\%\) . The whole system uses 55 watts of power, 33 watts in the optical DAS system, and 22 watts in the FPGA. Relying on its end-to-end interdisciplinary design, FiberFlex seamlessly combines fiber-optic sensors with FPGA accelerators to enable low-power real-time security monitoring without compromising privacy, making it a valuable addition to the existing security monitoring network. According to FiberFlex, more valuable research can be conducted in the future, such as fall monitoring for the elderly, migration of identification networks between different application scenarios, and improvement of anti-interference performance in more complex environments. In future perception networks, where the “eyes” are not feasible, let's use fiber optic touch instead.