Continuous deformation measurement for track based on distributed optical fiber sensor

Delamination propagation in fibrous laminates is often accompanied by development of a damage zone, within which intact fibers bridge the crack faces that constitutes a major toughening mechanism. A direct experimental assessment of the crack closure tractions due to bridging fibers is a challenging task. In addition, when the length of the bridging zone becomes comparable with the laminate linear dimensions, referred to as large scale bridging, the extent of bridging can be influenced by the laminate size and layup. Hence, traction-separation relations used in computational methods to predict fracture do not always rely on solid experimental evidence of large scale bridging and for numerical convenience simple linear traction-€“separation expressions are often considered. In this work, a semi-experimental methodology is employed to identify the contribution of large scale bridging to delamination resistance of unidirectional and cross-ply carbon fiber/epoxy laminates. Double cantilever beam specimens of different thickness are prepared and subjected to monotonic and fatigue mode I loadings. In selected specimens, optical fibers with multiplexed fiber Bragg grating (FBG) sensors are embedded. The strain distribution in the vicinity of the crack plane is monitored by FBG sensors. The strain data are used in an inverse identification procedure to quantify the bridging tractions and the energy release rate (ERR) due to the bridging. Subsequently, the identified tractions are implemented in a cohesive zone model to simulate delamination propagation. Employing the aforementioned approach, specimen thickness dependence of large scale bridging in delamination of unidirectional laminates is investigated. The results indicate that onset of delamination is independent of the specimen geometry. However, the extent of the bridging zone and the plateau level of delamination resistance both significantly increase with increasing the specimen thickness. The maximum traction at the crack tip is independent of the specimen size while the length of the steady state bridging zone scales linearly with the thickness of laminate. Moreover, the nonlinear rate of tractions decay is inversely proportional to the specimen thickness. It is shown that the tractions decay parameter is controlled by the curvature of the specimen arm. Evaluation of bridging effects on fatigue delamination of unidirectional laminates indicates that the stable phase of fatigue crack growth is more extended in the thicker specimens and the critical ERR increases by increasing the specimen thickness. Similar to the trend observed in monotonic delamination, fatigue delamination in the thicker specimens is accompanied by a longer bridging zone in which the bridging tractions decay with a lower rate. The maximum bridging traction at the crack tip is independent of the specimen thickness. It is demonstrated that the traction-separation relation and the ERR due to the large scale bridging depend on the ply orientations at the crack interface whereas ERR values at the onset of delamination are almost the same. As compared to delamination of a unidirectional specimen, crack propagation in cross-ply laminate is accompanied by a smaller bridging zone, but fiber bridging in the cross-ply specimens is more efficient. The adopted methodology in this work provides a robust assessment of large scale bridging and is a step towards a better understanding of delamination in fiber reinforced composites.

We report on the use of a frequency-rnodulated continuous wave (FMCW) technique for multiplexing fiber Bragg grating (FBG) sensors. This technique is based on the modulation of light intensity from a broadband source by a linear swept-frequency RF carrier. Signals from the FBG sensors located at different positions in an array are separated in frequency-domainand demodulated using a tunable optical filter. The potential and limitation of the technique are discussed. A 3-sensor FMCWmultiplexed FBG array of parallel topology and a 6-sensor hybrid FMCWIWDM system were experimentally demonstratedwith -30 dB crosstalk between sensors and 2 tc resolution in terms ofroot-mean-square strain value.Keywords: optical fiber sensors, fiber Bragg grating sensors, frequency-modulated continuous wave, strain measurements. 1. Introduction Fiber Bragg gratings (FBGs) have been identified as an enabling technology for a variety of applications from aerospace, marine, to civil engineering structural monitoring, from medical, nuclear power industry, to electrical power industry[1]. In many applications such as condition monitoring of civil structures such as highways, bridges and buildings. FBG sensorsneed to be multiplexed in order to achieve quasi-distributed measurement and to reduce cost per sensing point to compete withconventional electrical or optical sensors.The most popular technique for multiplexing FBG sensors is the wavelength division multiplexing (WDM) technique[2,3]. The maximum sensor number that can be multiplexed using the WDM technique is determined by the ratio ofthe sourcespectral width over the spacing between the Bragg wavelengths of the FBGs. For applications requiring a larger multiplexinggain, a combination ofthe WDM and time-division multiplexing (TDM) techniques [3] may be used.A TDM system employs a pulsed or gated CW source to illuminate gratings in sequence as the pulse travels along thefiber, the reflected signals from the gratings are separated in time domain [4]. The physical separation between the gratings isdetermined by the input pulse width. A fast time-gated photodetector can be used to select the signal from an individual grating

Thermal modeling of the power transformer to estimate the internal temperature only reflects the winding hot spot to a certain extent. The application of optical fiber sensors to measure transformer internal temperature directly can obtain the true temperature with high accuracy and stability and overcome the shortcomings of traditional electric sensors. This paper analyses the measuring principle of FBG (Fiber Bragg Grating) sensor, and constructs transformer internal temperature measurement platform with FBG temperature sensors, then adopt short-circuit method to simulate the process of winding heat under different load conditions. Finally the measurement of transformer internal temperature is conducted respectively with FBG temperature sensors and thermocouples. Comparison and analysis of the results shows that FBG sensors can effectively measure the internal temperature of the transformer.

Performance assessment of discrete wavelet transform for de-noising of FBG sensors signals embedded in asphalt pavement

In this paper, one large Fiber Bragg Grating (FBG) sensor sensing network for bridge local health monitoring was studied, designed and implemented to Harbin Sifangtai Bridge, China. According to the hot point principle and finite element analysis results, FBG sensors installation scheme was made to monitor the local strain of key elements, respectively. 60 FBG sensors had been installed in monitoring points along the height of I-shape girders distributed in three main monitoring sections along the bridge. Monitoring results in load testing period was obtained, the static and dynamic results show that FBG sensors can monitoring the local strain precisely, and the local strain response is in the safe range under test vehicle load. Monitoring results in service period was obtained, 9 months after load test. Results show that maximum local strain responses in three main monitoring sections are in the safe range, under the dynamic vehicle load. Comparison of the monitoring results from FBG sensor sensing network are used to unveil the local health condition of Sifangtai Bridge reflected by local strain response.

Fiber optic sensors (FOS) in long-term structural health monitoring (SHM) have drawn significant attention due to their pivotal role in detecting defects and measuring structural performance in diverse infrastructures. While using FOS, temperature variation due to environmental factors is still considered one of the major challenges to isolating sensing parameters. To address this issue, we reported a machine learning (ML)-augmented multi-parameter sensing system that enables simultaneous detection of strain and temperature effects based on one single fiber Bragg gratings (FBGs) sensor for SHM. The initial phase entailed designing, fabricating, and characterizing a novel FBG sensor in the laboratory, incorporating a set of four FBGs, each distinguished by distinct Bragg wavelengths. In the next phase, ML algorithms are employed to separate temperature effects from strain variations. As a proof of concept, mechanical loading tests are conducted on the sensor, exposing the FBG portion to various temperature conditions. In the final phase, data collected from a post-tensioned concrete bridge embedded with both strain and temperature FBG sensors are utilized, and the developed ML models are applied to observe real-environment outcomes. Despite the limited feature points of collected FBG spectrums, the developed ML models effectively address cross-sensitivity issues induced by temperature perturbations. The long-term benefit of using FOS is that it will enable a better understanding and utilization of aging infrastructure. This will potentially reduce embodied carbon of infrastructure in the future and assist in the global efforts to achieve Net-Zero.

Structural Health Monitoring (SHM) of aircraft structures, especially composite structures, has assumed increased significance on considerations of safety and costs. With the advent of co-cured structures, wherein bonded joints are replacing bolted joints there is a concern regarding skin-stiffener separation, which might not be detected unless a rigorous non-destructive testing (NDT) is done. It would hence be necessary to be able to detect and assess skin-stiffener separation in composite structures before it reaches the critical size. One of the health monitoring strategies is through strain monitoring using fibre optic strain sensors such as Fibre Bragg Grating (FBG) sensors. The first aspect that needs to be addressed is the characterization of the FBG sensors. Issues of embedment in composites have also to be addressed. Before evolving a damage detection strategy, the sensitivity of the structural strain to skin-stiffener separations must be clearly understood and quantified. This paper presents the analysis and experiments done with a composite test box to study the effect of skin-stiffener separation on the strain behaviour. The box consists of two skins stiffened with spars made of Bi-Directional (BD) glass-epoxy prepreg material. The spars are bolted to the skins and removing suitable number of bolts simulates 'de-bonds'. The strains of the healthy box are compared with the unhealthy box. The strains in the experiments are monitored using both strain gauges and Fibre Bragg Grating (FBG) sensors. The experimental results show that there is significant change in the measured strain near and away from the debond location. The finite element analysis of the box is done using ABAQUS and the analysis is validated with the experimental results. A neural network based methodology is developed here to detect skin-stiffener debonds in structures. A multi-layer perceptron (MLP) neural network with a feed forward back propagation algorithm is used to determine the size/severity of damage. The FE model is used to generate the neural network training data for various sizes of debonds. The results show that the network is able to predict the damage size well. The network is implemented for a specified load. However, it is seen that the damage size predicted is independent of the applied load and the network performance is dependent on the fidelity of the finite element model used to train the network.

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

Recently, it has become necessary to develop a monitoring technology that combines an FBG (fiber Bragg grating) sensor as a means for continuously monitoring whether the reinforcing effect of an FRP (fiber-reinforced polymer) is maintained on FRP-reinforced structural members. However, most existing research focuses on the insertion of FBG sensors into bar-shaped FRPs, and there is a lack of studies that analyze the details for an FRP strip combined with FBG sensors. In this regard, this paper seeks to develop a reinforcement for an NSM (near-surface-mounted) retrofit in which an optical fiber with an FBG sensor is combined with FRP strips. For this, a series of experiments were performed to find the adhesive strength of optical fiber-epoxy-FRP interfaces, the tensile strength of the FBG sensor part of optical fiber with a reflection lattice, and the sensing performance depending on the adhesion length between the optical fiber and the FRP strips. As a result of the study, the adhesion length not less than 20 mm in one direction from the center, with a total adhesion length of 40 mm, needs to be secured when the optical fiber with an FBG is attached and fixed between the two FRP strips with epoxy. In addition, it is expected that the proposed model can be used to properly predict the strain transfer of an FRP strip with a fiber optic sensor and can also be utilized when determining optimum dimensions.

Fiber Bragg grating (FBG) sensors have attracted a considerable amount of interest for their superior characteristics. However, the FBG sensors made on bare fibers are easily damaged. For their safe use in engineering, the glass core of optical fibers is coated with softer low modulus protective coatings. A portion of the host material strain is absorbed by the protective coatings when the strain transfers from the host material to the fiber core, and hence only a segment of structural strain is sensed. By introducing the shear modulus of the host material, a novel analytical model is developed for evaluating the sensing strain of the embedded FBG sensors in composite structures based on the strain in a host material. The average strain transfer ratio is deduced to describe the percentage transferred to the optical fiber core from the host material. It is concluded that the shear modulus of the host material influences strain transmission, especially when it is much lower than the modulus of the fiber core. Then, the strain transfer ratio of an optical fiber sensor embedded in a multilayered structure is developed in a similar way. The factors that affect the efficiency of strain transfer on the optical fiber sensor are deduced and discussed in detail based on the theoretical analysis. Finally, the theoretical results are verified through laboratory experimentation with the FBG sensors.

We have studied health-monitoring systems using embedded optical fiber sensors for damage detection in FRP (Fiber Reinforced Plastics) composite laminates. For sensing of strain or temperature changes in these composite materials, we developed the FBG (Fiber Bragg Grating) sensors using the polyimide-coated small-diameter optical fiber with cladding diameter of 40 micrometers , which can be embedded in FRP composite laminates without inducing any structural defects. The FBG was subjected to high-temperature heat treatment at 300 degree(s)C in order to suppress the optical characteristics change of the FBG over a long period of time. Even after such high temperature treatment, the FBG retained sufficient mechanical strength and reflectivity. We embedded the FBG into FRP composite laminates and measured the optical characteristics against temperature and tensile strain. These experimental results show this type of the embedded FBG is very promising for practical health-monitoring systems. In addition, we have investigated various multipoint measuring systems and propose the time division multiplexing system using these FBG sensors.

A review of our recent work on ultrahigh resolution optical fiber sensors in the quasi-static region is presented, and their applications in crustal deformation measurement are introduced. Geophysical research such as studies on earthquake and volcano requires monitoring the earth’s crustal deformation continuously with a strain resolution on the order of nano-strains (nɛ) in static to low frequency region. Optical fiber sensors are very attractive due to their unique advantages such as low cost, small size, and easy deployment. However, the resolution of conventional optical fiber strain sensors is far from satisfactory in the quasi-static domain. In this paper, several types of recently developed fiber-optic sensors with ultrahigh resolution in the quasi-static domain are introduced, including a fiber Bragg grating (FBG) sensor interrogated with a narrow linewidth tunable laser, an FBG based fiber Fabry-Perot interferometer (FFPI) sensor by using a phase modulation technique, and an FFPI sensor with a sideband interrogation technique. Quantificational analyses and field experimental results demonstrated that the FBG sensor can provide nano-order strain resolution. The sub-nano strain resolution was also achieved by the FFPI sensors in laboratory. Above achievements provide the basis to develop powerful fiber-optic tools for geophysical research on crustal deformation monitoring.

Embedded fiber Bragg grating (FBG) sensors are attractive for in-situ structural monitoring, especially in fiber reinforced composites. Their implementation in metallic structures is hindered by the thermal limit of the protective coating, typically a polymer material. The purpose of this study is to demonstrate the embedding of FBG sensors into metals with the ultimate objective of using FBG sensors for structural health monitoring of metallic structures. To that end, ultrasonic additive manufacturing (UAM) is utilized. UAM is a solid-state manufacturing process based on ultrasonic metal welding that allows for layered addition of metallic foils without melting. Embedding FBGs through UAM is shown to result in total cross-sectional encapsulation of the sensors within the metal matrix, which encourages uniform strain transfer. Since the UAM process takes place at essentially room temperature, the industry standard acrylate protective coating can be used rather than requiring a new coating applied to the FBGs prior to embedment. Measurements presented in this paper show that UAM-embedded FBG sensors accurately track strain at temperatures higher than 400 °C. The data reveals the conditions under which detrimental wavelength hopping takes place due to non-uniformity of the load transferred to the FBG. Further, optical cross-sectioning of the test specimens shows inhibition of the thermal degradation of the protective coating. It is hypothesized that the lack of an atmosphere around the fully-encapsulated FBGs makes it possible to operate the sensors at temperatures well above what has been possible until now. Embedded FBGs were shown to retain their coatings when subjected to a thermal loading that would result in over 50 percent degradation (by volume and mass) in atmospherically exposed fiber.

Fiber Bragg Grating (FBG) sensor is a new type of optical sensor that is light in quality, small in volume, easy to achieve distributed measurement, and easy to embed. When FBG sensor technology is introduced to the damage detection of the CFRP shaft, the shortcomings of conventional damage detection technologies, including ultrasonic testing and radiographic testing, can be effectively overcome. Considering the working load, operating environment and life requirements of the CFRP shaft , combined with the relevant national and industry standards for composite material testing, Prepare three CFRP shaft with embedded fiber gratings (non-destructive CFRP shaft, single hole CFRP shaft , double hole CFRP shaft), the hole diameter is 1mm, and the layering of CFRP shaft is ([0 / 90 / 45 / 0 / 0 / 45 / -45 / 0 / -45 / 0 / 90 / 90 / 0 / -45 / 0 / -45 / 45 / 0 / 0 / 45 / 90 / 0 ]). FBG is embedded between 18 and 19 layers, pasted on the surface of CFRP shaft with 10 FBG sensors (named FBG1, FBG2... FBG10). The strain information of the CFRP shaft is obtained through fiber grating to obtain the deformation of the CFRP shaft under a three-point bending load. Each CFRP shaft is loaded 12 times (A1, A2, A3, B1, B2, B3, C1, C2, C3, D1, D2, D3). Through the BP neural network method, the mapping relationship between strain distribution and damage model was established. In the model, the FBG sensor measures the strain of the CFRP shaft and forms the input of the neural network. Combined with the damage situation of the composite material, the learning samples of the network formed. After learning and training, the neural network can damage recognition. To better verify the accuracy of the neural network to distinguish and identify the CFRP shaft non-destructive, single-hole, and double-hole damage patterns. The finite element analysis was used to simulate the damage of three CFRP shafts under the same experimental conditions, and extract the eigenvalues at the same position to form the input of the network, combined with the damage situation of the composite material, constitutes a learning sample of the network. The experiment shows that the CFRP shaft experiment based on BP neural network verifies the reliability of the CFRP shaft damage recognition method. The finite element analysis is used to extract the strain information of CFRP shaft measuring points with different damage modes. The damage position information and strain information were input into the BP neural network. The experimental results are in good agreement with the simulation results, which verifies the accuracy of the BP neural network to distinguish the CFRP shaft non-destructive, single-hole, and doublehole damage patterns.

The article describes process of implementation of optical Fiber Bragg Grating (FBG) sensors into the composite wing structure and their behavior during the strength test. The wing is of all-composite construction. The upper and lower skins are made of glass/epoxy composite. The spar caps are made of carbon/epoxy unidirectional composite. Optical fibers were integrated directly into the spar caps and into the adhesive joints. They were oriented in parallel with the main spar axis. The first optical fiber with chain of multiple FBG sensors was integrated into the structure of upper spar cap. Another FBG chain of FBG sensors was located in the adhesive joint of lower spar cap and shear web. The wing was instrumented with strain gages as well. Strain gages were glued to the sides of the spar caps. Static load was produced by a hydraulic actuator. Experimental results from strain gages and FBG sensors were compared with the results of the analytical analysis of the wing.