Fiber Bragg Grating Sensor Network Research Articles

Wavelength detection of serial WDM Ultra-short fiber Bragg grating sensor networks based on a CCD interrogator using Deep Belief Networks and Sparrow Search Algorithm

Wavelength detection of serial WDM Ultra-short fiber Bragg grating sensor networks based on a CCD interrogator using Deep Belief Networks and Sparrow Search Algorithm

Optimized demodulation of highly overlapped fiber Bragg grating sensor networks using two-stage deep learning approach

Optimized demodulation of highly overlapped fiber Bragg grating sensor networks using two-stage deep learning approach

Design of a high-precision current source system for tunable laser driving

Fiber Bragg Grating (FBG), which is an Optical Fiber Sensor with high sensitivity, light weight, and strong anti-interference ability, can realize the measurement of physical quantities, such as temperature and strain. The sensor network formed by FBG sensors is through wavelength division multiplexing, which greatly reduced the difficulty of sensor wiring. Therefore, FBG sensors are widely used in sensing fields such as aerospace, bridges and tunnels. As a wavelength-modulated Optical Fiber Sensor, FBG’s sensing accuracy depends not only on the performance of the laser, but also on the stability of the driving current. In this paper, a high-precision current source system for laser S7500 modulation drive is designed, with a resolution of output current is higher than 2uA. and the laser S7500 is continuously linearly scanned at 20pm intervals in the wavelength range of 1527nm to 1567nm. The wavelength accuracy of the output optical signal is better than 2pm. The test results prove that the system has the advantages of high modulation speed, good stability and high reliability, and can be applied to high-precision FBG sensor networks.

Heartbeat and respiration monitoring based on FBG sensor network

A fiber Bragg grating (FBG)-based acquisition method for heartbeat and respiration was designed for portable home-based cardiopulmonary physiological parameter detection. The ballistocardiogram (BCG) is susceptible to noise interference during the acquisition process. In order to improve the accuracy of heart rate and respiration rate solving, the adaptive K-value-based variational modal decomposition (K-VMD) algorithm is proposed. First, the algorithm obtains the optimal solution by calculating the difference frequency of the last component of the neighboring K-values. Second, a threshold value is set according to the frequency variation. Again, the optimal K value is automatically selected according to the threshold. The problem of over-decomposition or under-decomposition can be solved by using the optimal K-value decomposition. Finally, heart rate and respiration rate are estimated using the power spectrum based on Welch's method. In 15 comparison experiments, the K-VMD algorithm was in perfect agreement with the ECG signal 13 times, and the other three conventional methods were 9, 7, and 5 times, respectively. The maximum error of the K-VMD algorithm was 1 bpm, and the other three conventional methods were 2 bpm, 3 bpm, and 3 bpm, respectively. The maximum error in the respiratory rate detection of this algorithm in the position of the diaphragm is ±0.2 bpm. It can be seen that the present algorithm can effectively reduce noise interference and improve monitoring accuracy. And it can provide data support for obtaining the characteristic waveform of cardiac shock signals and respiratory signals. It has certain practical applications.

Evolution of process-induced strain/stress field in plain woven composite antenna reflector by fusing multiscale numerical model and experimental data

This paper investigates the evolution of the process-induced strain/stress field in plain woven composite antenna reflectors by fusing the numerical simulation and the experimental data. In the numerical simulation, a new multiscale curing process model of plain woven composite reflectors is proposed for process-induced strain/stress prediction. Specifically, representative volume elements at three scales are established and solved to capture the effective properties of plain woven composites. Further, a novel interface layer with cure-dependent parameters is used to emulate the tool-part interaction. In the experiment, the fiber Bragg grating sensor network is embedded into the composite reflector to measure the process-induced strains. Some of the measurements are fused into the curing process model by the optimization-based fusion method. The fused model was used to predict the full-field strain and stress, and the results indicate that areas with larger curvatures experience greater strain and stress due to the interaction between the tool and the part.

Resolution enhancement for interrogating fiber Bragg grating sensor network using dilated U-Net.

In the fiber Bragg grating (FBG) sensor network, the signal resolution of the reflected spectrum is correlated with the network's sensing accuracy. The interrogator determines the signal resolution limits, and a coarser resolution results in an enormous uncertainty in sensing measurement. In addition, the multi-peak signals from the FBG sensor network are often overlapped; this increases the complexity of the resolution enhancement task, especially when the signals have a low signal-to-noise ratio (SNR). Here, we show that deep learning with U-Net architecture can enhance the signal resolution for interrogating the FBG sensor network without hardware modifications. The signal resolution is effectively enhanced by 100 times with an average root mean square error (RMSE) < 2.25 pm. The proposed model, therefore, allows the existing low-resolution interrogator in the FBG setup to function as though it contains a much higher-resolution interrogator.

Efficient reconstruction scheme with deep neural network for highly compressive sensing of fiber Bragg grating spectrum

In this work, we propose an efficient reconstruction scheme for compressive sensing (CS) of fiber Bragg grating (FBG) spectrum. Taking advantage of the sparse reflection spectrum of the FBG array network, we have demonstrated the use of CS for compressing the spectrum at an excessively high compression factor up to 64. In addition to that, the spectral difference (SD) of the spectra is used to further enhance their sparsity for the CS model. In this investigation, four different configurations have been devised and tested to compare their performance and effectiveness. Configuration IV that is based on SD and deep neural network offers the best recovery performance. The proposed method is a potential tool for efficient data storage and transmission for FBG sensor network.

High-precision demodulation method for the cobweb FBG sensor network.

To improve the demodulation accuracy and speed of the cobweb fiber Bragg grating (FBG) sensor network, a demodulation algorithm based on a one-dimensional (1D) dilated convolutional neural network (CNN) combined with improved wavelet adaptive threshold de-noising is proposed. The improved wavelet adaptive threshold de-noising algorithm is used to de-noise several highly overlapping sensing signals for accurately measuring optical fiber sensing signals. Using a well-trained 1D dilated CNN model achieves extremely low signal demodulation errors, even with highly overlapping signals. Experiments show that the demodulation scheme improves the detection accuracy of the cobweb FBG sensor network and shortens detection time. Determination of the peak wavelengths of the four highly overlapping sensing signals achieves a root-mean-square error of better than 0.10pm and an average demodulation time of 15.2ms.

Noise Signal Recognition and Noise Reduction Algorithm of Ships

Based on the study of the sensing property of fiber bragg grating (FBG) sensor network, this paper takes hybrid wavelength division/time division multiplexing sensor network with high-capacity FBG as the research object to study noise source, noise category and traditional noise reduction algorithm. In accordance with the characteristic of crosstalk accumulation noise in the network, this paper proposes LCEEMD-LWT signal processing method, which uses the local complementary ensemble empirical mean decomposition (LCEEMD) method for signal preprocessing, and then adopts LWT technology to refine high-frequency signal and solve the related problems. Then this paper put forwards detrended fluctuation analysis (DFA) to evaluate the sensing signal of high-capacity FBG, preprocesses the sensing signal by LCEEWD-LWT method, and conducts a temperature sensing experiment. The average temperature error of the demodulating system is reduced from 0.2923℃ to 0.2357℃, which not only overcomes the shortcoming that the traditional signal processing method is not meticulous for high-frequency signal processing, but also avoids the problem that the signal processing time is too long, exerting a good effect on the signal pre-processing of high-capacity FBG. The method in this paper has certain theoretical and technical reference value for the signal processing and the improvement of sensing property of high-capacity FBG sensing demodulating system in engineering application.

Simultaneous Strain and Temperature Discrimination in 18650 Li-ion Batteries Using Polarization-Maintaining Fiber Bragg Gratings

In this work, a fiber Bragg grating (FBG) sensor network inscribed in a polarization-maintaining (PM) fiber is proposed to proceed with a multipoint simultaneous temperature and strain discrimination in different locations (positive and negative terminals, and middle) on a cylindrical Li-ion battery. The birefringence property of the PM fibers, together with FBG sensors, allowed such an application using only one fiber line fixed to the edges of the battery. The battery was subjected to two different charge/discharge cycles, one with nominal charging and discharging conditions (1.00 C and 1.13 C, respectively) and another with abusive conditions (1.88 C for charge and 2.39 C for discharge). The PM-FBG sensors registered maximum temperature and strain variations at the end of the abusive discharge process of the battery; the positive terminal achieved a 28.7 ± 0.3 °C temperature variation, while the center achieved 221 ± 10 με strain variation. The results indicate a different strain variation behavior in the middle location when compared to the negative and positive terminals, as well as a higher temperature variation in both terminals when compared to the middle location. The use of PM-FBG sensors successfully demonstrates their feasibility in locally tracking and discriminating strain and temperature shifts in a battery surface. To our knowledge, this is the first study using the application of PM-FBG sensors to monitor and discriminate critical safety parameters in Li-ion batteries.

Self-Healing and Shortest Path in Optical Fiber Sensor Network

In this study, a new square-based fiber Bragg grating (FBG) sensor network model is proposed to address possible link failures in FBG sensor networks and improve their reliability. Graph theory and optical switching are simultaneously applied to these sensor networks to improve their self-healing ability; the FBG sensor network is regarded as a directed graph. Three commonly used self-short-circuit algorithms are compared in terms of the self-healing capabilities that they provide to the optical fiber sensor network. Among these, the shortest-path faster algorithm achieved a high, nearly 90% repair accuracy and had an average repair time of 0.103 s, the shortest in this study. The newly designed FBG self-healing network can be reorganized and repaired when local damage occurs, thereby improving its reliability.

Impact localization with a weighted spectral cross correlation method

The impact localization is a typical inverse problem to identify the damage source of structures. To overcome the positioning problems of the low resolution caused by sparse reference points, a weighted spectral cross-correlation (WSCC) method is proposed. First, a mathematical model with a series of Fiber Bragg Grating (FBG) sensors is built to generate a database of transient frequency responses of each reference point when random shock happens. Second, to identify the spectrum difference between the impact point and the reference point, a cross correlation model is built by mean and normalization operations on the frequency domain. Third, to figure out the impact point among the adjacent reference points, bicubic spline interpolation is realized by subdividing the grid of the WSCC. To validate the accuracy of our suggested method, an impact localization system is designed with a multi FBG sensor network and a composite laminate. The experimental result shows that the error of our suggested method is almost ten times lower than that of other traditional methods. The weighted spectral cross-correlation has advantages in the field of structural health monitoring.

Multi FBG sensor-based impact localization with a hybrid correlation interpolation method

To locate the impact source of a composite material structure with a series of fiber Bragg grating (FBG) sensors, a hybrid correlation interpolation method is proposed, in this paper, based on a weighted correlation analysis and a unidirectional cubic spline interpolation. First, the signals collected by FBG sensors are preprocessed on the frequency domain to reduce noise interference. Second, the impact similarity map is built by using the weighted cross-correlation value to estimate the location of the impact roughly. Third, the similarity map is further subdivided by the unidirectional cubic spline interpolation to increase the impact localization. Fourth, the FBG sensor network is experimental analyzed to optimize the layout and the structure of the FBG sensor network. The validity of the multi FBG sensor-based impact localization method is verified on a carbon fiber composite plate with a size of 650 × 500 (mm). The experimental result shows that the mean absolute error is 9.74 mm for our suggested method and 15.51 mm for the bicubic polynomial interpolation (BPI) algorithm, which is reduced by 5.77 mm. The maximum errors are 36.40 mm for the BPI algorithm while are reduced to 21.55 mm for our suggested method. Furthermore, the effect of FBG sensor combination layout and number on impact location accuracy is analyzed and compared. It shows that the sensor network layout of rectangular structure using four FBG sensors is a better choice after the comprehensive consideration of economic benefits, localization accuracy, and error rate.

Research on Deformation Reconstruction Based on Structural Curvature of CFRP Propeller With Fiber Bragg Grating Sensor Network

The deformation and reconstruction of the composite propeller under the static load in the laboratory is studied so as to provide the basic research for the deformation and reconstruction of the underwater deformed propeller. The fiber Bragg grating (FBG) sensor is proposed to be used for strain monitoring and deformation reconstruction of the carbon fiber reinforced polymer (CFRP) propeller, and a reconstruction algorithm of structural curvature deformation of the CFRP propeller based on strain information is presented. The reconstruction algorithm is verified by using variable-thickness CFRP laminates in the finite element software. The results show that the relative error of the reconstruction algorithm is within 8%. Then, an experimental system of strain monitoring and deformation reconstruction for the CFRP propeller based on the FBG sensor network is built. The propeller blade is loaded in the form of the cantilever beam, and the blade deformation is reconstructed by the strain measured by the FBG sensor network. Compared with the blade deformation measured by three coordinate scanners, the reconstruction relative error is within 15%.

Design of Fiber Bragg Grating Sensor Networks

This study explores the effective use of a spectral area defined by a radiation source for multipoint measurements with fiber Bragg grating (FBG) sensors. The capacity of the sensor network based on a wavelength multiplex is limited by the spectral work area of the used radiation source and by several other parameters, such as the spectral parameters of individual sensors, type of the measured quantity and measurement range, sensitivity coefficients, production tolerances, and protection zones among the measuring channels. This is why it is necessary to use this limited area in an “economical” manner. The initial part of this article explores modeling of sensor networks using Bragg gratings, an output of which is an analysis of the impact of individual parameters on the capacity of a sensor network. These models are then applied to the mathematical definition of the given network proposal formed by real sensors. At the end, the stated principles are verified by real measurements with a sensor network formed by five Bragg sensors. The study points to the necessity of paying close attention to the proposals of sensor networks with FBG for the purpose of the effective use of the given spectral work area, increasing capacity, and reducing the possibility of crosstalk when assessing the Bragg sensors.

Design of Cobweb Fiber Bragg Grating Sensor Network Based on Gated Circulation Unit

Design of Cobweb Fiber Bragg Grating Sensor Network Based on Gated Circulation Unit

Research on thermal stiffness of machine tool spindle bearing under different initial preload and speed based on FBG sensors

As a key parameter, stiffness determines the dynamic characteristics of both the bearing and the spindle unit. However, interactions between the spindle speed, initial preload, and cutting force caused by the thermal expansion of spindle unit components alter the bearing stiffness characteristics during the running process. Aiming to carry out real-time online monitoring of the thermal characteristics and their effect on the machine tool spindle bearing stiffness, a fiber Bragg grating (FBG) sensors network was proposed. Further, the effects were considered under various axial loads, combined axial, and specific radial load to study bearing temperature increase, thermally induced preload, and thermal stiffness. The results have shown that the bearing thermal stiffness of the bearing decreases with the increase of the spindle speed. Moreover, an optimal initial preload can be selected with respect to the speed to maximize the thermal stiffness. Finally, the thermally induced preload additionally pre-tightened the bearing.

Single Peak Fiber Bragg Grating Sensors in Tapered Multimode Polymer Optical Fibers

This research demonstrates how low loss commercially available multi-mode (MM), perfluorinated polymer optical fibers (POFs) can be used for robust and reliable fiber Bragg grating (FBG) based sensing. A single peak FBG reflection spectrum is achieved by tapering the fiber to a diameter just small enough to make the fiber effectively single-moded and then inscribing the FBG in the waist section of the taper. Here we use plane-by-plane, direct writing inscription with a femtosecond laser. In particular we used the GigaPOF-50SR MM fiber and tapered it by a factor of 0.24, which was found to be the critical ratio that provided a single grating reflection peak. The GigaPOF-50SR fiber was chosen because it allows operation at 1550 nm and has a suitable small 50 μm core diameter, which minimizes the required taper ratio and thus the insertion loss of the taper. The FBG sensor was found to have a strain and relative humidity sensitivity of 20 nm/%strain and ∼ 6.7 pm/%RH, respectively. The grating proved to be largely insensitive to temperature. This approach to single-mode POF sensor fabrication will enable multi-point strain sensing at 1550 nm over 50 m long fiber. It combines the main advantages of single mode POFs (robust FBG sensing) and MM POFs (low transmission loss) and demonstrates that tapered MM POFs constitute an efficient solution for the development of robust and reliable long distance POF FBG sensor networks.

Multi-peak detection algorithm based on wavelength feature recognition in FBG sensor networks

We propose an improved Gaussian curve fitting method based on the Hilbert transformation (HTG) to tackle the ineffectiveness of the traditional peak-seeking algorithm in detecting the multi-peak Fiber Bragg grating (FBG) reflection spectra. A five-point sliding filter is used to process the FBG reflection spectral signal, de-noise and smooth the noise, and select the optimal threshold point by the Hilbert transformation (HT). The sub-spectra of the multiple FBG reflection spectral signals were derived and the initial positioning of the spectral peaks were achieved. The Levenberg–Marquardt (LM) algorithm is used to extract the Bragg wavelength from the segmented sub-spectral signals as well as optimize the Gaussian curve fitting coefficients. The HTG-LM algorithm is then proposed, and is optimized and utilized to achieve precise positioning of the spectral peaks. The theoretical analysis and experimental results showed that the proposed HTG-LM algorithm could dynamically detect the multiple reflection spectra of the FBG sensing system with good stability, and at the same time, reduce the amount of peak-seeking data, which is highly beneficial to improve the signal demodulation rate. The peak detection accuracy of the proposed algorithm is better than 1 pm and the precision is better than 4 × 10 − 7 pm, which indicates that this HTG-LM algorithm provides an accurate demodulation algorithm for the FBG sensor networks. As a result, it is a promising multi-peak detection algorithm proposed by this paper to be applied to the FBG sensing systems.

Reliable self-healing FBG sensor network for improvement of multipoint strain sensing

In this paper, we proposed and designed reliable self-healing fiber Bragg grating (FBG) sensor network for improvement of multipoint strain sensing. We use an optical switch (OSW) to reconfigure the sensor network and improve the self-healing function during fault happen in the sensor network. In this work, to prove and validate the detection performance of our proposed Bragg wavelength detection technique even when the number of overlap spectra of FBGs increases, we conduct three experiments and collect three different cases of strain data (namely case 1, case 2, and case 3) by applying different strain steps to FBGs. In case 1 experiment, strain was applied to only FBG1 sensor, while the other four FBGs keeps fixed. In case 2 experiment, strain was applied to FBG1 and FBG2 sensors at the same time and with different strain steps. In case 3 experiment, strain was applied to FBG1, FBG2 and FBG3 sensors at the same time and with different strain steps. As a result, in all cases of the experiment, three situations of spectra were introduced between FBGs like non-overlapped, partially overlapped and completely overlapped spectra. To solve this overlap problem, we used deep learning technique to accurately identify the Bragg wavelength of FBGs in the condition of the partially or fully overlapped spectra. Therefore, our proposed FBG sensor system can improve the reliability and detection accuracy of the sensor system even the number of overlaps FBGs spectra increases.