Fiber Bragg Grating Data Research Articles

Enhancing Roads Infrastructure Monitoring with FBG Sensors and Multi-Threshold Signal Tracking

Accurate monitoring techniques are needed to improve pavement durability by ensuring their structural integrity and longevity. This study developed an innovative real-time monitoring system utilizing fiber Bragg grating (FBG) sensors embedded in asphalt layers. The data collected by FBG sensors includes a baseline primarily influenced by temperature and peak shape data associated with short-term loading from passing vehicles. Therefore, analyzing FBG data and identifying statistical patterns poses a challenge. To address this challenge, employing a signal tracker proves to be a valuable option for monitoring the baseline and scanning signals for subsequent analysis. Moreover, considering the varying loading weights on FBG sensors due to diverse vehicles, tracking FBG signals across multiple thresholds enhances precision. A MATLAB-based program was developed incorporating a multi-threshold signal tracker capable of efficiently processing FBG data by tracking signals in both positive and negative multi-thresholds. This tracker effectively distinguishes between different loading events and extracts the baseline signal after tracking. Subsequently, the system provides outputs, including loading distribution, number of loading events, traffic, and temperature effects. Finally, to validate the system, real FBG data collected during an extensive monitoring campaign was processed using the developed program. The obtained outputs contribute to a comprehensive assessment of pavement performance, thereby facilitating the development of more accurate maintenance and rehabilitation strategies.

Hybrid model based on Fiber Bragg Grating and fluorescence intensities ratio for strain and temperature distinction

Fiber optic sensors, including Fiber Bragg Grating (FBG) networks, are widely used to monitor the structural health (SHM) of infrastructure such as buildings, bridges, and tunnels. However, their multiple sensitivities make it difficult to isolate the specific impacts of each measured parameter on FBG, compromising the accuracy of the data. To overcome this challenge, we propose an innovative approach that combines FBG with the principle of Fluorescence Intensity Ratio (FIR). This combination allows for a more accurate analysis of wavelength variations in FBG, thus facilitating the distinction between temperature and deformation impacts. Additionally, we are developing a matrix formula to accurately determine temperature and deformation from FBG and FIR data, thereby improving the reliability of structural monitoring. Our results demonstrate the effectiveness of this hybrid approach through experiments and simulations, highlighting its importance in optimizing the performance of SHM devices. By integrating these two technologies, our method allows for a better understanding of parameter variations, leading to more reliable and accurate monitoring of infrastructure structural health.

Fiber-Bragg-Grating-Based Sensor System to Measure Battery State of Charge Based on a Machine Learning Model

Real-time monitoring of the state of charge (SOC) of the batteries used in a wide variety of applications is becoming increasingly important, especially given the impetus by the current targets towards “net-zero”. In this research, an advanced approach was used involving fiber Bragg grating (FBG)-based sensors that were developed and implemented for the measurement of the key parameters required to ensure optimum battery performance. In this work, one of the biggest challenges to assess (and then map) the data from the sensor system developed is tackled in order to better understand the key parameters of the battery in an efficient and improved way. It is well known that the relationship between the changes in the resonance wavelength of the FBGs used in the sensor system, arising due to change in the electrical parameters of the battery, is complex and dependent on several different factors. In this work, this effect was evaluated by coupling the sensor data to a data-driven regression model approach that was developed for the measurement of the SOC of the batteries used, and this was obtained directly and conveniently from the FBG data. In this comprehensive study, FBG-based sensors were fabricated and then installed onto the battery, which then was subjected to a range of charging–discharging cycles, following which the electrical parameters of the battery were estimated from recorded data using a black-box machine learning (ML) model. Data-driven regression algorithms were employed for the training of the black-box model. The efficiency of the estimation of the SOC of the battery from the FBG-based sensor data was found to be high, at 99.62% (R2 values of Estimated SOC and True SOC line), creating a very satisfactory result for this key measurement. Thus, the work shows the robustness of the FBG-based sensor system combined with the neural network algorithm as an effective way to evaluate the electrical parameters of the battery, which is particularly important, as no physical/electrochemical/electrical model of the system is thus required.

Measurement Technique and Result Analysis of Helicopter Rotor Blade Structural Vibration Load

The measurement of helicopter rotor blade structural load amid flight has always been the difficulty in flight test. In this paper, the principle of the existing blade structural load measurement method (electrical measurement method) was analyzed, and the problem of physical decoupling in the use of this method was expounded. As a weak signal measurement, the electrical measurement method also has electromagnetic interference problems, which will affect the flight test period of blade structural load measurement. Therefore, a numerical decoupling measurement method based on fiber Bragg grating (FBG) was proposed. Then, the new method was applied and verified in the load equation modeling test and the flight test under the real atmospheric environment was carried out. Through comparing and analyzing the measured data of the new method and the electrical measurement one, the correctness of the FBG data decoupling method was validated. The results indicate that the method proposed in this paper can effectively improve the efficiency of blade load equation modeling engineering and has good application value.

AI Prediction of Brain Signals for Human Gait Using BCI Device and FBG Based Sensorial Platform for Plantar Pressure Measurements.

Artificial intelligence (AI) in developing modern solutions for biomedical problems such as the prediction of human gait for human rehabilitation is gaining ground. An attempt was made to use plantar pressure information through fiber Bragg grating (FBG) sensors mounted on an in-sole, in tandem with a brain-computer interface (BCI) device to predict brain signals corresponding to sitting, standing and walking postures of a person. Posture classification was attained with an accuracy range between 87–93% from FBG and BCI signals using machine learning models such as K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and naïve Bayes (NB). These models were used to identify electrodes responding to sitting, standing and walking activities of four users from a 16 channel BCI device. Six electrode positions based on the 10–20 system for electroencephalography (EEG) were identified as the most sensitive to plantar activities and found to be consistent with clinical investigations of the sensorimotor cortex during foot movement. A prediction of brain EEG corresponding to given FBG data with lowest mean square error (MSE) values (0.065–0.109) was made with the selection of a long-short term memory (LSTM) machine learning model when compared to the recurrent neural network (RNN) and gated recurrent unit (GRU) models.

Spall length estimation based on strain model and experimental FBG data

Bearings are critical components in most machines that suffer from fatigue damage, and hence need to be monitored. The development of spalls in the bearing races is the prevalent failure mode. In this research, we propose a new diagnostic method for ball bearings monitoring that is based on strain measurement. A standard Fiber Bragg Grating (FBG) sensor has been used for the strain measurement. A physics-based theoretical analysis is used to establish insight and analysis of the strain signals generated by both healthy and faulty bearings. The analysis is based on two complementary models that were integrated into a strain model: a quasi-static Finite element (FE) and a solid bodies dynamic model. The integrated strain model enables simulation of a wide range of spall lengths and describes the behavior of the strain signals as a result of various phenomena. The strain model was validated with measured data. A new signal processing scheme of strain signals for the diagnostics of bearings was established. This scheme enabled the development of a novel automatic algorithm for spall length estimation, which makes it possible, for the first time, to track spall length propagation in bearings outer race, based on strain signals. The algorithm was developed and validated based on seeded fault experiments, wherein different sizes of faults on the outer race at different rotational speeds were examined. The trend of the spall length during a bearing endurance test was generated and verified. The insights from the strain model, combined with the experimental observations, enable generalization of the algorithm and definition of its capabilities and limitations.

Fiber Bragg grating sensor-based temperature monitoring of solar photovoltaic panels using machine learning algorithms

Fiber Bragg Grating (FBG) sensors are an emerging and prominent optical sensing technology of accurately measuring strain, depth, temperature, density, and several physical parameters. Due to high solar radiation, the increased solar panel temperature affects photovoltaic cell efficiency. Hence, monitoring the temperature of solar panels and providing proper cooling is essential to attain optimal electrical performance. FBG sensor is used to monitor the solar panel temperature in this research. The accuracy and stability of the peak search algorithms in the acquired experimental data are analyzed. Reisz fractional-order derivative and Savitsky-Golay filter are improved using a decision tree regressor to determine the peak and noisy spectrum with different signal-to-noise ratios. The algorithm surpassed the demerits of conventional peak algorithms used for the FBG spectrum. This work implements the fractional derivatives and a machine learning algorithm initially denoised. The number of epochs for linear regression is 15. The depth allowed for decision trees is 1000, and the number of estimators for the random forest is 150. A peak is detected with a decision tree regressor trained on clean and noiseless FBG data. Machine learning algorithms such as linear regression, random forest, and decision tree are compared. The decision tree regressor with hyperparameter tuning yields the best results with high accuracy of 99.83% compared to other conventional peak detection methods. The statistical tests and metaheuristic algorithms are also performed to assess the experimental outcome.

A Time Synchronization Method Between FBG Deformation Measurement System and Airborne DPOS Based on Waveform Matching

The airborne Distributed Position and Orientation System (DPOS) is an important means for multi-point motion measurement required for earth observation with multi-task sensing loads. The sub-nodes in DPOS rely on the main node to perform transfer alignment for them to get high-precision motion data, which requires accurate deformation data and the relative motion relationship between different nodes. Fiber Bragg Grating (FBG) is one of the main means of deformation measurement at present. However, the time synchronization error between FBG and DPOS directly restricts the accuracy of transfer alignment. For this, a high-precision time synchronization method based on waveform matching is presented. Firstly, the data correlation between FBG and DPOS is analyzed. Then the main time error factors, sampling frequency and time delay are determined. On this basis, the empirical mode decomposition and wavelet analysis are used to extract and match the vibration data from the FBGS and bending angle data calculated by DPOS, thus updating the time-varying sampling frequency and time delay of FBGS, and finally achieving high-precision time synchronization between FBGS and DPOS. Results of ground dynamic experiment show that the standard deviation and mean value of FBG time error is less than 4ms and 2ms respectively, and the deformation displacement error of synchronized FBG data is less than 1%, which provides a prerequisite for high-precision transfer alignment of DPOS.

Health Monitoring of Aerospace Structures Utilizing Novel Health Indicators Extracted from Complex Strain and Acoustic Emission Data.

The development of health indicators (HI) of diagnostic and prognostic potential from generally uninformative raw sensor data is both a challenge and an essential feature for data-driven diagnostics and prognostics of composite structures. In this study, new damage-sensitive features, developed from strains acquired with Fiber Bragg Grating (FBG) and acoustic emission (AE) data, were investigated for their suitability as HIs. Two original fatigue test campaigns (constant and variable amplitude) were conducted on single-stringer composite panels using appropriate sensors. After an initial damage introduction in the form of either impact damage or artificial disbond, the panels were subjected to constant and variable amplitude compression–compression fatigue tests. Strain sensing using FBGs and AE was employed to monitor the damage growth, which was further verified by phased array ultrasound. Several FBGs were incorporated in special SMARTapesTM, which were bonded along the stiffener’s feet to measure the strain field, whereas the AE sensors were strategically placed on the panels’ skin to record the acoustic emission activity. HIs were developed from FBG and AE raw data with promising behaviors for health monitoring of composite structures during service. A correlation with actual damage was attempted by leveraging the measurements from a phased array camera at several time instances throughout the experiments. The developed HIs displayed highly monotonic behaviors while damage accumulated on the composite panel, with moderate prognosability.

Damage identification using deep learning and long-gauge fiber Bragg grating sensors.

This study proposes an innovative structural damage identification method using dynamic response measured by long-gauge fiber Bragg grating (FBG) sensors and accelerometers to train deep convolutional neural networks (DCNNs). At the same time, the pre-trained model is applied to another structure through transfer learning (TL) technology. To verify this method, the I-shaped steel beam vibration test was conducted at first. Three types of data, acceleration, wavelength, and the fusion of the former, are utilized to train convolutional neural network (CNN) models, and the models are then tested and compared. In TL, another CNN is pre-trained using FBG data of steel beam. Next, the data of T-shaped reinforced concrete (RC) beam are employed to train the pre-trained model. The performance of the CNN is evaluated by training history and the confusion matrix. The results show the CNN-based damage identification method can classify the damage pattern accurately. The CNN trained by fusion data has both high classification accuracy and faster training speed. TL technology can greatly reduce the training time of other target tasks in the same fields.

Detection, Localization and Quantification of Impact Events on a Stiffened Composite Panel with Embedded Fiber Bragg Grating Sensor Networks.

Nowadays, it is possible to manufacture smart composite materials with embedded fiber optic sensors. These sensors can be exploited during the composites’ operating life to identify occurring damages such as delaminations. For composite materials adopted in the aviation and wind energy sector, delaminations are most often caused by impacts with external objects. The detection, localization and quantification of such impacts are therefore crucial for the prevention of catastrophic events. In this paper, we demonstrate the feasibility to perform impact identification in smart composite structures with embedded fiber optic sensors. For our analyses, we manufactured a carbon fiber reinforced plate in which we embedded a distributed network of fiber Bragg grating (FBG) sensors. We impacted the plate with a modal hammer and we identified the impacts by processing the FBG data with an improved fast phase correlation (FPC) algorithm in combination with a variable selective least squares (VS-LS) inverse solver approach. A total of 164 impacts distributed on 41 possible impact locations were analyzed. We compared our methodology with the traditional P-Inv based approach. In terms of impact localization, our methodology performed better in 70.7% of the cases. An improvement on the impact time domain reconstruction was achieved in of the cases.

Laboratory core flooding experiments in reservoir sandstone under different sequestration pressures using multichannel fiber Bragg grating sensor arrays

This paper describes an innovative method for a fiber-optic sensing technology based on multichannel fiber Bragg grating (FBG) sensor arrays, which was applied to deionized water and free-CO2 core flooding experiments on saturated Shaximiao sandstone core specimens under various sequestration pressures. Three FBG sensor arrays, bonded to the sandstone surface along the axial and radial directions, dynamically monitored the strain responses during the wetting phase (water) or non-wetting phase (CO2) displacement processes. It was found that with the increase in the confining pressure or pore pressure, the relative strain increased throughout the experiment. For the water flooding experiment, the proportions due to rise the sequestration pressures are different in each scenario and FBG arrays, and they are generally positive increase, which can also be ascribed to the effective pressure. Meanwhile, the initial arrival time of the precisely characterized strain histories revealed the fronts of the injected water as well as the detailed implications of the CO2 plume migration during the core-scale flooding process. These FBG data from water/CO2 flooding experiments are of great significance for geophysicists and reservoir engineers to better establish the accumulations of raw data and theoretical mechanism reserves for further experimental analysis, numerical simulation and field applications in the CCUS projects and can enhance the minimal experimental data available in the literature to provide a new data set in the geomechanical field for water-CO2 two-phase fluid flow.

Digital monitoring and health diagnosis for mechanical equipment operation safety based on fiber Bragg grating sensor

This paper introduces fiber Bragg grating (FBG) based on a fiber optic grating sensor developed to be embedded on mechanical equipment for digital monitoring and health diagnosis. The theoretical and experimental researches on the new-style FBG sensor (FBGS) technology, high-speed demodulation, and data transmission are discussed. The transmission characteristics between the FBG and the detection interface, modeling and compensation method for online distributed multi-parameter digital monitoring and methods for data processing, synchronous sampling, and long-term dynamic digital monitoring using embedded technology are also presented. The acquired information by an FBGS can be used for the optimization of maintenance schedules and refinement of mechanical equipment design. It is a challenge to gather real-time data from components working at high speed and in a severe environment of high temperature, high pressure, and high rotation speed. Currently, there are no sensors or technologies available for digital monitoring and health diagnosis under this rigorous situation for use in mechanical engineering operation safety. As a result, this paper introduces an online distributed and integrated digital monitoring system and health diagnosis. The new principle and new method will contribute to modern measurements in science and technology, mechanical engineering, and large mechanical equipment operation safety.