Fiber Bragg Grating Sensors Research Articles

Advances in Fiber Bragg Grating (FBG) Sensing: A Review of Conventional and New Approaches and Novel Sensing Materials in Harsh and Emerging Industrial Sensing

Advances in Fiber Bragg Grating (FBG) Sensing: A Review of Conventional and New Approaches and Novel Sensing Materials in Harsh and Emerging Industrial Sensing

Optimal Design of a Sensor Network for Guided Wave-Based Structural Health Monitoring Using Acoustically Coupled Optical Fibers.

Guided waves (GW) allow fast inspection of a large area and hence have received great interest from the structural health monitoring (SHM) community. Fiber Bragg grating (FBG) sensors offer several advantages but their use has been limited for the GW sensing due to its limited sensitivity. FBG sensors in the edge-filtering configuration have overcome this issue with sensitivity and there is a renewed interest in their use. Unfortunately, the FBG sensors and the equipment needed for interrogation is quite expensive, and hence their number is restricted. In the previous work by the authors, the number and location of the actuators was optimized for developing a SHM system with a single sensor and multiple actuators. But through the use of the phenomenon of acoustic coupling, multiple locations on the structure may be interrogated with a single FBG sensor. As a result, a sensor network with multiple sensing locations and a few actuators is feasible and cost effective. This paper develops a two-step methodology for the optimization of an actuator-sensor network harnessing the acoustic coupling ability of FBG sensors. In the first stage, the actuator-sensor network is optimized based on the application demands (coverage with at least three actuator-sensor pairs) and the cost of the instrumentation. In the second stage, an acoustic coupler network is designed to ensure high-fidelity measurements with minimal interference from other bond locations (overlap of measurements) as well as interference from features in the acoustically coupled circuit (fiber end, coupler, etc.). The non-sorting genetic algorithm (NSGA-II) is implemented for finding the optimal solution for both problems. The analytical implementation of the cost function is validated experimentally. The results show that the optimization does indeed have the potential to improve the quality of SHM while reducing the instrumentation costs significantly.

Analysis of Bare Uniform Fiber Bragg Grating Sensor for Measuring Strain on the Landing Gear of the LSU-02 Unmanned Aircraft

Analysis of Bare Uniform Fiber Bragg Grating Sensor for Measuring Strain on the Landing Gear of the LSU-02 Unmanned Aircraft

Advanced data-driven FBG sensor-based pavement monitoring system using multi-sensor data fusion and an unsupervised learning approach

Pavement health monitoring and assessment using sensor technology provides valuable insights into the condition of roads and helps in effective maintenance and management. This paper presents an advanced fiber Bragg grating (FBG) sensor-based pavement monitoring system that leverages multi-sensor data fusion techniques and an unsupervised learning approach for enhanced structural health assessment. The study commences with the fusion of data from various FBG sensors based on specific considerations and employing a novel function to improve the accuracy of pavement condition assessment. A dedicated data management system is introduced to address the challenges associated with continuous monitoring. This system automates data organization, pre-processing, and storage, facilitating historical trend analysis and post-processing. Then, the research introduces a novel unsupervised learning approach utilizing the density-based spatial clustering of applications with noise (DBSCAN) algorithm. This algorithm identifies abnormal patterns in the FBG sensor data, which may indicate structural anomalies or unexpected events, aiding the operator by automatically flagging abnormal data. By incorporating principle component analysis (PCA)-based feature fusion, the system reduces the dimensionality of the data, which enhances the efficiency of the clustering process. Various anomalies were detected in the data collected for four months from the test track. The cause and relevance of these anomalies were assessed to facilitate informed decision-making. This approach enables the operator to achieve early detection of potential structural issues and distinguish abnormal damage data from other anomalous data, thereby contributing to the overall success of the pavement monitoring system. In conclusion, the integration of advanced FBG sensor technology with multi-sensor data fusion and unsupervised learning techniques results in a sophisticated and robust pavement monitoring system. The presented approach enhances accuracy by detecting anomalies at any strain level, computational time using multi-sensor data fusion, and reliability of structural health assessment through anomaly evaluation. It also provides a framework for proactive decision-making, ultimately improving the longevity and structural integrity of critical infrastructure.

Experimental Study of Sinkhole Propagation Induced by a Leaking Pipe Using Fibre Bragg Grating Sensors.

Sinkhole formation caused by leaking pipes in karst soluble rocks is a significant concern, leading to infrastructure damage and safety risks. In this paper, an experiment was conducted to investigate sinkhole formation in dense sand induced by a leaking pipe. Fibre Bragg grating (FBG) sensors were used to record the strain. A balloon was gradually deflated within a bed of wet silica sand to create an underground cavity. Eighteen FBG sensors, with a wavelength range between 1550 nm and 1560 nm, were embedded horizontally and vertically in the physical model at different levels to monitor deformation at various locations. A leaking pipe was installed to induce the collapse of the formed arch above the cavity. The strain measurements suggested the following four phases in the sinkhole formation process: (1) cavity formation, (2) progressive weathering and erosion, (3) catastrophic collapse, and (4) subsequent equilibrium conditions. The results showed differences in the strain signatures and distributions between the horizontal and vertical measurements. During the critical phase of the sinkhole collapse, the horizontal measurements primarily showed tension, while the vertical measurements indicated compression. This investigation demonstrates the effectiveness of FBGs as advanced monitoring tools for sinkhole precursor identification. The study also suggests using FBGs in geotechnical monitoring applications to improve the understanding and mitigation of sinkholes and related geohazards.

Characterization of Thermal Expansion Coefficient of 3D Printing Polymeric Materials Using Fiber Bragg Grating Sensors.

This work aims at the determination of the coefficient of thermal expansion (CTE) of parts manufactured through the Fused Deposition Modeling process, employing fiber Bragg grating (FBG) sensors. Pure thermoplastic and composite specimens were built using different commercially available filament materials, including acrylonitrile butadiene styrene, polylactic acid, polyamide, polyether-block-amide (PEBA) and chopped carbon fiber-reinforced polyamide (CF-PA) composite. During the building process, the FBGs were embedded into the middle-plane of the test specimens, featuring 0° and 90° raster printing orientations. The samples were then subjected to thermal loading for measuring the thermally induced strains as a function of applied temperature and, consequently, the test samples' CTE and glass transition temperature (Tg) based on the recorded FBG wavelengths. Additionally, the integrated FBGs were used for the characterization of the residual strain magnitudes both at the end of the 3D printing process and at the end of each of the two consecutively applied thermal cycles. The results indicate that, among all tested materials, the CF-PA/0° specimens exhibited the lowest CTE value of 14 × 10-6/°C. The PEBA material was proven to have the most isotropic thermal response for both examined raster orientations, 0° and 90°, with CTE values of 117 × 10-6/°C and 108 × 10-6/°C, respectively, while similar residual strains were also calculated in both printing orientations. It is presented that the followed FBG-based methodology is proven to be an excellent alternative experimental technique for the CTE characterization of materials used in 3D printing.

Demodulation of Different Quantities of Overlapping Spectra in FBG Sensors Based on Combined Conv-TasNet and LSTM

Demodulation of Different Quantities of Overlapping Spectra in FBG Sensors Based on Combined Conv-TasNet and LSTM

Deflection estimation of reinforced concrete beams using long-gauge optic sensors

Deflection is a crucial indicator in structural health monitoring, directly impacting the service life, safety, and economic viability of structures. However, current indirect methods for measuring deflection, such as multi-element FBG sensors or accelerometers, often have limitations. While they may be suitable for flexure-dominated reinforced concrete (RC) specimens in the elastic stage, they neglect the significant influence of nonlinearity and shear deformation. This paper introduces a new approach for estimating the deflection of reinforced concrete (RC) beams using distributed long-gauge optic sensors. The method tackles the limitations of current methods by incorporating the crucial factors of shear deformation, neutral axis position variation and shear stiffness degradation. Experimental studies are first conducted to validate the accuracy and stability of long-gauge optic sensors in small-scale RC members. Algorithms are then utilized the measurement data to estimate the deflection. The results demonstrate the excellent performance of long-gauge optic sensors in measuring shear deformation and vertical deflection of RC beams, as evidenced by comparisons with LVDT measurements. Additionally, a method for decoupling flexural and shear deformation is proposed. This method offers a preliminary assessment of the reinforced concrete (RC) beam type by quantifying the relative significance of shear deformation.

Research on FBG curvature sensor based on PET substrate and silicone package

To address the low repeatability and accuracy in single package form and lack of strain transfer theory analysis in composite material form of fiber Bragg grating (FBG) curvature sensors, a flexible curvature sensing test scheme for FBG based on polyethylene terephthalate (PET) substrate and silicone package is proposed. Firstly, the factors affecting the strain response of the FBG are obtained through the analysis of the multilayer strain transfer theory, and the relationship between different material encapsulation forms and the strain response of the FBG is explored through finite element simulation. Secondly, the FBG curvature sensor with optimized simulation parameters is fabricated in package, and the curvature calibration verification experiments are performed on the sensor. Finally, the experimental results show that the deviation index of the FBG curvature sensor is between −1.89 % and 1.62 %, which indicates a better repeatability consistency. Based on the computation, the maximum sensitivity of the FBG curvature sensor is 13.32 µε/m−1 when the spacing between the fiber and the substrate layer is 2.50 mm, and its error is 0.30 % comparing with the simulation of the same condition, which supports the accuracy of the theoretical analysis and simulation of the multilayer strain transfer of the encapsulated FBG sensor conducted in this paper, meanwhile, the optimal encapsulation process of the FBG curvature sensor is obtained.

Dynamic analysis and shape sensing of pipes subjected to water hammer by the inverse finite element method

Pipe structures are commonly employed in modern industries. However, the transient pressure induced by water hammer leads to a high risk of structural damage, highlighting the importance of Structural Health Monitoring (SHM). The inverse Finite Element Method (iFEM) offers a way to reverse structural deformations based on a set of discrete strain data. In this paper, the water hammer phenomenon in a Reservoir-Pipe-Valve (RPV) system is investigated. The element iQS4 is adopted to reconstruct dynamic deformations for the pipe using iFEM. The water hammer resulting from an instantaneous closure of the valve can generate significant fluid pressure, leading to severe pipe vibrations. The vibration deformations can be monitored by iFEM. The maximum error of the pipe nodal displacement is below 3 % and the average error is below 2 % in the proposed several sensor location cases, even the mesh of iFEM is coarser than the FE model. Among the sparse sensor location cases, the X path which is suitable for Fiber Bragg Grating (FBG) sensors has the highest accuracy of the displacement reconstruction. The results reveal a promising prospect in the real-time monitoring based on iFEM for pipes.

Wavelet denoising analysis on vacuum-process monitoring signals of aerospace vacuum vessel structures

Abstract The monitoring of micro-defects or external actions induced vacuum degradation in aerospace vacuum vessels is an important challenge. A vacuum-process monitoring method based on quasi-distributed fiber Bragg grating (FBG) sensing technology is proposed. Due to the influence of environmental noise and vacuum pump operation noise, the raw signals measured by FBG sensors contain a large amount of noises, which affects the measurement accuracy and data analysis. Therefore, the wavelet threshold (WT) denoising method is proposed to analyze the influence of noises on the monitoring signals measured by two different kinds of FBG sensors. The evaluation of the monitoring signals after denoising indicates that the proposed method can effectively remove the noise and significantly improve signal quality. The highest signal-to-noise ratio of the processed signals can reach 37.61 dB and the mean square error is 3.68 × 10−7, while retaining the key features of the original signal. The proposed WT denoising method demonstrates better performance and feasibility compared with moving average filtering and Kalman filtering methods. The study provides critical supports for improving the performance and reliability of the vacuum vessel monitoring system.

A tailless FBG smart bolt with good performance

A novel type of tailless FBG smart bolt with good performance is developed. Owe to the innovative design of an integrated FBG sensor comprising a strain measured FBG and a temperature-compensated FBG, the tailless FBG smart bolt serves the purpose of torque monitoring or bolt looseness monitoring. We use a theoretical approach to establish the linear correlation between the applied torque and the central wavelength of the tailless FBG smart bolt. Additionally, a newly designed M24 bolt with a nominal strength has been integrated as a crucial component of the tailless FBG smart bolt. To enable the experimental study, the tailless FBG smart bolt is newly designed, fabricated and tested. The experimental results show a strong linear correlation between the applied torque and the central wavelength of the tailless FBG smart bolt, showing good agreement with the theory.

Strain Measurement for Composite Materials During Mechanical Testing Based on Fiber Bragg Grating Monitoring

Composite materials are widely used as repair materials in the oil and gas sector, particularly for vessels, piping, and pipelines. Inspection and monitoring of these composite repairs is a critical aspect of ensuring their integrity and long-term performance. The reason being that failure of composite repairs may lead to loss of production and incur additional costs for re-repair or equipment replacement. Conventionally, a visual inspection is generally performed to observe any irregularities on the external surface of the composite repairs. This will usually be followed by a series of Non-destructive Tests (NDT) to identify the extent of the damage. However, these NDT methods, merely allowing off-line testing in a local manner with complicated and heavy equipment, require extensive and time-consuming labour. Structural Health Monitoring (SHM) based on Fibre Bragg Grating (FBG) that utilizes strain-based signals is one of the best options for monitoring the damage mechanism in composite due to its unique advantages of light weight, high stability and reliability, a long-life cycle, and the ability to capture the online reading. In this paper, the application of the FBG monitoring approach was used to examine the strain characteristics of composite materials based on mechanical tests (tensile and flexural). Three types of samples were prepared to simulate the type of damage mechanism: healthy, delamination, and disbondment. Based on the results, it was shown that the FBG sensor has potential for detecting and distinguishing the types of failure mechanisms in composites, including cracks, delamination, and disbondment. Hence, it was suggested that the FBG sensor technology could be used for site deployment in industry.

Development and engineering application of fiber bragg grating intelligent cable in large-span hyperbolic parabolic spatial cable network

In order to accurately control the prestress force of cables in long-span cable net structures, a new type of fiber Bragg grating (FBG) intelligent cable was developed. The FBG sensor was directly encapsulated inside the cable, and the sensing performance of the intelligent cable was verified by the cyclic tensile tests. The results show that the sensitivity coefficients of FBG monitoring are basically consistent, and the linearity deviation of the fiber Bragg grating is less than 4.67 %. Based on a real project Shunde Desheng Sports Center, a 114 m long-span saddle-shaped cable net structure model was established using finite element analysis, and the distribution and correlation of internal forces of cables in different tension stages were calculated and compared with the actual monitoring results of intelligent cables. The analysis results indicate that the force changes of intelligent cables is basically consistent with the value of finite element simulation. According to the data of long-term intelligent cable monitoring, the change of cable force caused by the temperature deformation of the steel ring beam is much greater than the influence of the temperature rise and fall of the cable itself on the cable force. Intelligent cables can provide a reference for cable force monitoring of long-span cable net structures.

Time-domain expression and mechanical response of the viscoelastic Poisson's ratio in asphalt pavement

Poisson's ratio (PR) is a fundamental mechanical parameter for materials. Unlike the constant Elastic Poisson's Ratio (EPR), the Viscoelastic Poisson's Ratio (VEPR) is significantly influenced by external factors such as environmental temperature, stress level, and loading frequency. Using the VEPR to characterize the stress state of asphalt pavements provides a more accurate reflection of their actual service conditions. Moreover, the VEPR plays a more critical role than the EPR in predicting fatigue life, analyzing temperature sensitivity, and optimizing pavement structure design. In this study, the Elastic-Viscoelastic Correspondence Principle (EVCP) and the Zener mechanical model were combined to derive a time-domain expression for the VEPR of asphalt pavement. Fiber Bragg Grating (FBG) sensors were embedded within various structural layers of an in-service road to collect mechanical response and temperature data under traffic loads. Based on the measured temperature data, Finite Element Models (FEMs) were developed for both the VEPR and EPR conditions. The mechanical responses were validated using these FEMs, and the results were compared with the strain data collected by the FBG sensors. The results indicated that both VEPR and EPR increase most rapidly between 20°C and 35°C, with VEPR resulting in higher transverse and longitudinal strains and stresses than EPR, while vertical strain remained constant. Furthermore, the VEPR was better suited to actual driving conditions, with a peak error of only 3.97 % and an average error of 10.04 %.

Sensor-Enhanced Thick Laminated Composite Beams: Manufacturing, Testing, and Numerical Analysis.

This study investigates the manufacturing, testing, and analysis of ultra-thick laminated polymer matrix composite (PMC) beams with the aim of developing high-performance PMC leaf springs for automotive applications. An innovative aspect of this study is the integration of Fiber Bragg Grating (FBG) sensors and thermocouples (TCs) to monitor residual strain and exothermic reactions in composite structures during curing and post-curing manufacturing cycles. Additionally, the Calibration Coefficients (CCs) are calculated using Strain Gauge measurement results under static three-point bending tests. A major part of the study focuses on developing a properly correlated Finite Element (FE) model with large deflection (LD) effects using geometrical nonlinear analysis (GNA) to understand the deformation behavior of ultra thick composite beam (ComBeam) samples, advancing the understanding of large deformation behavior and filling critical research gaps in composite materials. This model will help assess the internal strain distribution, which is verified by correlating data from FBG sensors, Strain Gauges (SGs), and FE analysis. In addition, this research focuses on the application of FBG sensors in structural health monitoring (SHM) in fatigue tests under three-point bending with the support of load-deflection sensors: a new approach for composites at this scale. This study revealed that the fatigue performance of ComBeam samples drastically decreased with increasing displacement ranges, even at the same maximum level, underscoring the potential of FBG sensors to enhance SHM capabilities linked to smart maintenance.

Modeling of Fiber Optic Acoustic Coupler for Ultrasonic Sensing

Abstract Fiber Bragg grating (FBG) sensors have been applied in the remote bonding configuration for structural health monitoring, in which ultrasonic modes are propagated along the optical fiber to the sensor. Recently coupling of the ultrasonic mode from optical fiber to optical fiber through an adhesive coupler has also been demonstrated. This paper develops a finite element (FE) model to describe the coupler behavior as a function of the fiber and coupler properties, for future optimization of couplers. The FE model is validated with previous experimental data. We also compare the FE model to three theoretical models (spring and damper frictional contact models and coupled mode theory). The results show that the FE model and spring well replicate the experimental results. Future work will use the resulting FE and spring models to derive solutions for the coupling coefficient as a function of the geometrical and material parameters of the coupler.

Wavelength-Dependent Bragg Grating Sensors Cascade an Interferometer Sensor to Enhance Sensing Capacity and Diversification through the Deep Belief Network

Fiber-optic sensors, such as fiber Bragg grating (FBG) sensors and fiber-optic interferometers, have excellent sensing capabilities for industrial, chemical, and biomedical engineering applications. This paper used machine learning to enhance the number of fiber-optic sensing placement points and promote the cost-effectiveness and diversity of fiber-optic sensing applications. In this paper, the framework adopted is the FBG cascading an interferometer, and a deep belief network (DBN) is used to demodulate the wavelength of the sampled complex spectrum. As the capacity of the fiber-optic sensor arrangement is optimized, the peak spectra from FBGs undergoing strain or temperature changes may overlap. In addition, overlapping FBG spectra with interferometer spectra results in periodic modulation of the spectral intensity, making the spectral intensity variation more complex as a function of different strains or temperature levels. Therefore, it may not be possible to analyze the sensed results of FBGs with the naked eye, and it would be ideal to use machine learning to demodulate the sensed results of FBGs and the interferometer. Experimental results show that DBN can successfully interpret the wavelengths of individual FBG peaks, and peaks of the interferometer spectrum, from the overlapping spectrum of peak-overlapping FBGs and the interferometer.

Evaluation of the Strengthening Effects on Prestressed Carbon-Fiber-Reinforced-Polymer-Strengthened Steel Beam Bridges Using Macro-Strain Influence Lines

Effectively evaluating the effectiveness of bridge strengthening is a necessary means to ensure the normal operation of existing strengthened bridges, especially when evaluating the effectiveness of bridge strengthening without interrupting normal traffic. Based on a distributed long-gauge Fiber Bragg Grating (FBG) sensor, this paper derived the macro-strain influence line (MSIL) formula for a simply supported beam bridge under a moving vehicle load, studied the changes in the MSIL at the bottom of the beam under the vehicle load before and after the prestressed CFRP plate strengthening, and proposed a rapid evaluation method for the strengthening effect based on the amplitude of the MSIL as the evaluation index for the strengthening effect. Finally, the prestressed CFRP-strengthened steel beam was tested under the moving vehicle load. The theoretical analysis and the experimental results confirm that under the load of moving vehicles, the macro-strain–time history amplitude of the strengthened steel beams under different prestressed tensioning conditions is different. The amplitude of the macro-strain time history of the strengthened bridge is reduced compared to before strengthening, and the local strengthening effect of the bridge can be monitored by the amplitude change in a single sensor. The change in global stiffness can be evaluated by monitoring the MSIL obtained from multiple long-gauge strain sensors.

A full computational framework for the temperature analysis of a flax-reinforced composite footbridge with field monitoring validation

This research presents a computational framework for predicting temperatures in flax-reinforced composite sandwich footbridges. The case study is a 15m-span footbridge situated in Almere, the Netherlands, instrumented with 82 fiber Bragg grating (FBG) sensors and 8 thermocouples within an IoT-based structural health monitoring (SHM) system, enabling real-time evaluation. The main focus of this article is to enhance the understanding of structural temperature distributions in FRP sandwich cross-sections for bridge engineering applications. Furthermore, providing a comprehensive and accurate framework for temperature normalization of FBG strain measurements. The methodology involves environmental data, point-by-point solar radiation analysis with sun sheltering, and thermal properties characterization, culminating in a numerical analysis using Abaqus. A k-d tree binary search is performed previous to the numerical analysis to solve compatibility between Abaqus and Rhino's meshing algorithms. The non-uniform heat flux is used as input for the numerical analysis and handled through UEXTERNALDB and DFLUX subroutines. The use of a sub-modeling technique, allows the isolation, in correspondence of the sensor locations, of one critical section of the bridge for detailed analysis. Verification against in-situ measurements demonstrates the framework's efficacy. It was concluded that the framework restitutes errors in the range of ±1 ÷ 3 °C across varying weather conditions, during different seasons, and consistently for multiple sensor locations. Finally, the results are used to compensate strain measurements of nearby selected FBG sensors from the temperature counterparts.