Fiber Bragg Grating Demodulation Research Articles

Study of hybrid integrated PLC-AWG chip for FBG demodulation

Fiber Bragg grating (FBG) sensors have been widely used in various fields. To develop a miniaturized, high-precision, and high-response FBG interrogation system, arrayed waveguide grating (AWG) has been widely used as a key device. In this paper, a 40-channel arrayed waveguide grating based on silica planar lightwave circuits (PLCs) is designed fabricated, coupled to a photodetector array using hybrid integration techniques. The hybrid-integrated demodulation chip enables precise demodulation of the FBG sensor wavelength in the C-band. The test results show that the AWG has a good transmission spectrum with a 3-dB bandwidth of 1.98 nm, insertion loss of about 3.5–4.1 dB, and adjacent channel crosstalk of about −7.84 dB. The established FBG interrogation system can realize continuous interrogation in the dynamic range of 1514.6–1578.6 nm, the wavelength resolution is 1 pm and the demodulation precision is 2.11 pm in the dynamic range of 1555.3–1556.1 nm.

Accurate and fast calibration for FBG demodulation based on deep learning and ensemble learning

Accurate and rapid demodulation plays a crucial role in fiber Bragg grating (FBG) sensing systems. The Fabry-Perot (F-P) filter is a dependable demodulation technique with excellent accuracy. However, the F-P filter suffers from demodulation error drift in a temperature-changing environment. The non-repeatable wavelength scanning affects the accuracy and stability of demodulation. To solve the problem, an accurate and rapid calibration approach is proposed for FBG demodulation. First, the temporal convolutional network (TCN) is utilized to extract the hidden information and long-term temporal relationships in the input features including temperature, a temperature changing rate, and shift of reference grating. Second, a state-of-the-art light gradient boosting machine (LightGBM) capable of forecasting demodulation error is adopted for rapid forecasting. To demonstrate the effectiveness of the proposed approach, the traditional TCN model and the composited-residual-block TCN (CTCN) model are both discussed, and the temperature-drift experiments are designed and conducted in two temperature-changing environments. The experimental results show that, compared to the widely applied long short-term memory (LSTM) model, the TCN-LightGBM model achieves higher prediction accuracy and reduces computation time by at least 59.68%. The proposed approach is an affordable and effective alternative to the existing hardware-based calibration techniques.

A modeling and calibrating method of FBG sensors for wing deformation displacement measurement

The airborne distributed Position and Orientation System (POS) is a key piece of equipment for providing high-precision motion parameters for aerial remote sensing systems. However, wing deformation degrades the performance of distributed POS, thus, it is urgent to obtain high-precision deformation information to assist distributed POS. In this study, a modeling and calibrating method of fiber Bragg grating (FBG) sensors for wing deformation displacement measurement is proposed. First, based on the cantilever beam theory and piecewise superposition, a modeling and calibrating method for wing deformation displacement measurement is established. The wing is then placed under different deformation conditions, and the changes in the wing deformation displacement and corresponding wavelength variations of the pasted FBG sensors are obtained using theodolite coordinate measurement system and FBG demodulator, respectively. Subsequently, linear least square fitting is deployed to develop the relationship model between the wavelength variations of the FBG sensors and wing deformation displacement. Finally, the wing deformation displacement at the measuring point in the temporal and spatial dimensions is obtained by fitting and interpolation. An experiment is conducted, and the results show that the accuracy of the proposed method can reach 0.721 mm with a wing length of 3 m, which can be used in the motion compensation of an airborne distributed POS.

Design and development of an economical temperature compensated bidirectional fiber Bragg grating flow sensor

A target-type cantilever based temperature compensated fiber Bragg grating (FBG) flow sensor is designed and developed with economical demodulation and data acquisition schemes. FBG is glued on the surface of a thin elastic stainless steel cantilever in pre-strained condition. The cantilever is mounted inside a flow channel by a special purpose brass holder with a height and direction adjustable threaded stem. The sensor is interrogated by an optical spectrum analyzer (OSA) and indigenously developed twin FBGs and chirped FBG—FBG intensity demodulation schemes. Response of the sensor is investigated in the flow rate range of 0–10 l min−1 in both forward and reverse flow directions. In spectral demodulation, the flow sensor offered a linear response in the full investigated range with a sensitivity of 19.2 pm/(L/min). However, the twin FBGs demodulation scheme presented a linear behaviour in the range of 4–10 l min−1 with sensitivities of 1.22 ± 0.03 μW/(L/min) and 29.89 ± 0.55 mV/(L/min) detected by the high speed power meter and photodetector, respectively. The novel CFBG-FBG intensity demodulator offered a linear response throughout its tested range and can offer a flow rate detection up to 265 l min−1 at the present sensitivity of the sensor with the condition that the cantilever-FBG system must be strong enough to withstand the higher flow rates. The temperature of the fluid is monitored by an FBG temperature sensing probe connected to the flow channel using intensity demodulation scheme by another pair of twin FBGs. Temperature compensation of the flow sensor is performed by arithmetic operations over the reflection intensities due to temperature probe to the results of the flow rates using a cost-effective data acquisition scheme.

Analysis of Strain Transfer in FBG-Based Embedded Optical Fiber 3-D Shape Sensor

The FBG-based embedded optical fiber 3-D shape sensor (FBG-EOFSS) composed of several different materials finds potential applications in various industrial and medical applications. The strain loss during the strain transfer affects the change of the fiber Bragg grating (FBG) sensor’s wavelength, which in turn deteriorates the position accuracy. In this article, first, we analyzed the strain transfer in FBG-EOFSS and derived the optical–mechanical transformation equation. Then, a strain transfer model combining mechanics, materials, and optics is developed based on the finite-element analysis (FEA) method. The FEA simulation results show that the strain transfer rate decreases by 27.7%/mm with the increase of the adhesive thickness under FBG. The optimal average strain transfer rate is about 0.97 when the elastic modulus of the adhesive is in the range of 109–1014 Pa. Moreover, the effect of the strain transfer on the position accuracy is analyzed. The relative position error of 3-D shape reconstruction increases by 8.4% with the strain transfer rate decreasing by 10%. Finally, an experimental system composed of the clamp, the displacement stage, and the FBG demodulator is developed. The strain sensitivity coefficients of bare FBGs and FBG sensors were tested quantitatively. The corresponding average strain transfer rate was calculated by the strain sensitivity coefficients. The results can potentially conduce to the design of 3-D shape sensors and improve the positioning accuracy.

Cascade Edge Filter for Demodulation of Quasi-Distributed FBG Sensor

The realization of multiple demodulation sche- mes of fiber Bragg grating (FBG) sensor is important for its engineering application. Here, we proposed a new edge filter that is formed by cascading dispersion compensation fiber (DCF) and no-core fiber (NCF) structure between two single-mode fibers (SMFs) for demodulation of quasi-distributed FBG sensor. The edge filter can be suitable for FBG with different center wavelengths by changing its structure parameters to achieve a wide range of FBG demodulation. In order to verify the performance of the edge filter, a demodulation system for quasi-distributed FBG sensor is successfully designed by introducing an optical time-domain reflectometer (OTDR), the wavelength shift of FBG caused by the change of temperature and strain is converted into power change through the edge filter, so as to realize temperature and strain demodulation. In the experiment, two FBG sensors separated by 500 m were set at the end of a 3000-m optical fiber for temperature and strain monitoring. The results show that the temperature sensitivity is 0.23 dB/°C from 50 °C to 100 °C, and the strain sensitivity is 0.01658 dB/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \epsilon $ </tex-math></inline-formula> from 0 to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$669.6\,\mu \epsilon $ </tex-math></inline-formula> . The edge filter for FBG demodulation has the advantages of high sensitivity, adjustable structural parameters, low cost, and independent multipoint/multiparameter demodulation.

Quasi-distributed FBG interrogation based on coherent wavelength-to-time mapping using mode-locked laser and pseudo-random barcode

A microwave photonic demodulation scheme for multiplexing fiber Bragg grating (FBG) sensing system based on coherent wavelength-to-time (WTT) mapping is proposed and demonstrated experimentally in this paper. With the help of mode-locked laser (MLL) source and dispersion element, the broadband pseudo-random barcode obtained from fiber random grating can be mapped into time domain to fulfill high-speed interrogation for FBGs. The shifts of FBG center wavelength will be transformed into the variations of filtered pseudo-random barcode, and finally converted into the time location changes of the cross-correlation peaks with narrow full-width half-maximum (FWHM), which is helpful to realize high-accuracy and high-resolution FBG demodulation utilizing oscilloscope. The experiment results exhibit good demodulation linearity and a strain interrogation sensitivity of 0.0302 ns/με in cascaded sensing network with a repetition rate of 20 MHz, corresponding to a strain measurement resolution of 2.05 με. Besides, the impacts of system noises and light source line-shapes on the demodulation performance are studied numerically, and the relevant solutions are proposed and verified respectively.

Soft joint shape measurement device based on FBG with a simple demodulating system.

Soft joint shape measurement is challenging because, in most cases, it relies solely on internal sensors. Existing shape estimation methods commonly take measurements at discrete points and utilize curve-fitting schemes, which are inefficient for complex joint shapes that require continuous measurements. Therefore joint shape measurement sensors rely on the fiber Bragg grating (FBG) due to its sensitivity, immunity to electromagnetic interference, and flexibility. Nevertheless, FBG demodulation is still an open research case. Hence, we propose a shape measurement device appropriate for FBG-based continuous measurements that employs a sensor with only three FBGs thrusting inside the soft joint to measure its 3D shape. Moreover, we develop a simple demodulating system exploiting the FBG's filter overlapping properties and design a calibrating process for FBG signals. Soft joint shape measurement experiments highlight our method's effectiveness, providing a relative error within 0.7%. Further tests involving continuum robot measurement reveal that the achieved precision is of the same level as a motion-capturing system.

Development of a Fault Detection Instrument for Fiber Bragg Grating Sensing System on Airplane.

This study develops a fault detection device for the fiber Bragg grating (FBG) sensing system and a fault detection method to realize the rapid detection of the FBG sensing system on airplanes. According to the distribution of FBG sensors on airplanes, the FBG sensing system is built based on wavelength division multiplexing (WDM) and space division multiplexing (SDM) technologies. Furthermore, the hardware and software of the fault detection device and the relevant FBG demodulator are studied in detail. Additionally, in view of the similar features of the healthy FBG sensor in the same measuring point, a rapid fault diagnosis method based on a synthetical anomaly index is proposed. The features (light intensity I, signal length L, standard deviation of original sample and energy value in time-domain P) of FBG sensors are extracted. The aggregation center value of the above feature values is obtained through the loop iteration method. Furthermore, the separation degrees of features are calculated and then form the synthetical anomaly index so as to make an effective diagnosis of the state of the FBG sensor. Finally, the designed fault detection instrument and proposed fault detection method are used to monitor the 25 FBG sensors on the airplane, the results indicated that three faulty and two abnormal FBG sensors on the airplane are identified, showing the effectiveness of the proposed fault detection method.

Review of fiber Bragg grating interrogation techniques based on array waveguide gratings

The photonic integrated interrogation technology based on array waveguide grating is a hot but difficult research area in the silicon optical field. Compared with traditional interrogation methods, the photonic integration interrogation technology based on an array waveguide grating has obvious advantages in high-speed and high-precision demodulation of fiber Bragg gratings due to its high demodulation accuracy, fast demodulation speed, and small package size. In recent years, with the development of photonic integration technology, various research institutions and relevant organizations have conducted extensive and in-depth research and optimization on the photonic integration interrogation method of array waveguide gratings. In this paper we introduce the working principle of array waveguide grating and the principle of fiber Bragg grating wavelength interrogation based on array waveguide grating, the important progress of fiber Bragg grating interrogator based on array waveguide grating in both material system and system performance, and summarize the typical applications in interrogator based on array waveguide grating. The future development of fiber Bragg grating demodulation system is proposed from three aspects: new materials, system integration, and scale-up, which provides a reference for the research and development of photonic integrated interrogation technology based on array waveguide grating.

Hysteresis and temperature drift compensation for FBG demodulation by utilizing adaptive weight least square support vector regression.

Hysteresis and temperature drift deteriorate the demodulation performance of tunable Fabry-Perot (F-P) filters. This study addresses a novel adaptive weight least square support vector regression (AWLSSVR) to compensate for the hysteresis and temperature drift of F-P filters. The temperature drift of a referent fiber Bragg grating(FBG) is used to estimate the temperature drifts of other three sensing FBGs, and a novel adaptive weighting strategy with an asymmetric noise interval is proposed, to eliminate the effects of noise in the training dataset. The experimental results show that when the temperature-changing modes of the training and testing datasets were close to each other, the error of the proposed method is reduced to 8.7 pm, while the errors of the other three conventional methods based on LSSVR are more than 10.8 pm. Further, when the temperature-changing modes of the training and testing datasets were partly different, the error of the proposed method was reduced to 5.4 pm, while the errors of other methods were more than 11.9 pm. It was verified experimentally that the proposed AWLSSVR method is more accurate and robust than other versions of WLSSVR for training samples with noise, requires no additional hardware, and covers the entire C band.

Low Velocity Impact Localization of Variable Thickness Composite Laminates.

Variable thickness composite laminates (VTCL) are susceptible to impact during use and may result in irreparable internal damage. In order to locate the internal impact damage of complex composite structures and monitor the impact signals of VTCL at the same time, a low velocity impact (LVI) monitoring system based on an optical fiber sensing network was constructed. Fiber Bragg grating (FBG) sensors are suitable for monitoring strain characteristics. By arranging FBG sensors on the laminate, we studied the spectrum analysis and localization of the impact signal collected by a FBG demodulator at constant temperature. The prior knowledge of variable thickness composite structures is difficult to obtain, and the multi-sensor dynamic monitoring is complex and difficult to realize. In order to locate the LVI of composite structures without prior knowledge, based on empirical mode decomposition (EMD), we proposed an impact localization method with zero-mean normalized cross-correlation (ZNCC) and thickness correction. The experimental results of LVI localization verification show that the ZNCC algorithm can effectively remove the temperature cross-sensitivity and impact energy influencing factors, and the thickness correction can reduce the interference of variable thickness characteristics on localization performance. The maximum localization error is 24.41 mm and the average error is 15.67 mm, which meets engineering application requirements. The method of variable-thickness normalization significantly improves impact localization performance for VTCL.

High-precision and wide-wavelength range FBG demodulation method based on spectrum correction and data fusion.

Optical fiber sensing technology plays an important role in the application of the sensing layer of the Internet of Things. The core of this technology is the demodulation of the fiber Bragg grating (FBG) sensing system. Since the FBG sensor utilizes the wavelength change to respond to the measured size, it is of great significance to improve the accuracy of the FBG wavelength demodulation. However, the demodulation performance of the current FBG wavelength demodulation method still has much room for improvement in terms of accuracy and stability. To this end, we propose a composite gas cell demodulation scheme based on spectrum correction and data fusion by using differential photodetectors, fitting extrapolation, data fusion methods, etc. The issue of low demodulation accuracy arising due to noise, temperature drift, spectral distortion, etc., was addressed to improve the demodulation performance of the FBG. In the experiment, four FBGs with different center wavelengths were used to verify their demodulation accuracy in the range of 1510-1590 nm. The maximum repeatability error of the FBG wavelength was measured to be 2.51 pm, and the linearity was as high as 99.9% or more; under the working environment of -20 °C to 60 °C, the maximum full-scale error did not exceed ±1.71 pm, which is improved by 54.3% compared with the traditional method.

A CNN-based FBG demodulation method adopting the GAF-assisted ascending dimension of complicated signal

We introduce the convolutional neural network (CNN) to extract the effective information of some complex signals in the fiber sensing, and verify the feasibility in the specific process of fiber Bragg grating (FBG) demodulation. In the experiments, in order to eliminate the cross interference in the energized coil, classical double grating configuration is adopted to distinguish magnetic field and temperature. At the same time, the introduction of thermal induced chirp (TIC) phenomenon produces more wavelength components to achieve more precise demodulation. The signal received by oscilloscope presents complex waveform, and the traditional analytical solution cannot demodulate it precisely, while the deep learning capabilities of neural networks can be leveraged to solve this problem. The one-dimensional (1D) time-series signal is transformed into a two-dimensional (2D) image by using the Gramian angle field (GAF) method. On this basis, the data are augmented according to the characteristics of signal noise. Five classical neural network structures are used to process the data. The root mean square errors (RMSEs) of the demodulation results can achieve 0.3 °C and 17.1 Gs respectively, which are relatively high precision in terms of intensity demodulation. This method can be extended to demodulate 1D frequency domain signals and wavelength domain signals, rendering a demodulation scheme to process some complex signals in optical fiber sensing effectively.

Dilated convolutional neural networks for fiber Bragg grating signal demodulation.

In quasi-distributed fiber Bragg grating (FBG) sensor networks, challenges are known to arise when signals are highly overlapped and thus hard to separate, giving rise to substantial error in signal demodulation. We propose a multi-peak detection deep learning model based on a dilated convolutional neural network (CNN) that overcomes this problem, achieving extremely low error in signal demodulation even for highly overlapped signals. We show that our FBG demodulation scheme enhances the network multiplexing capability, detection accuracy and detection time of the FBG sensor network, achieving a root-mean-square (RMS) error in peak wavelength determination of < 0.05 pm, with a demodulation time of 15 ms for two signals. Our demodulation scheme is also robust against noise, achieving an RMS error of < 0.47 pm even with a signal-to-noise ratio as low as 15 dB. A comparison on our high-performance computer with existing signal demodulation methods shows the superiority in RMS error of our dilated CNN implementation. Our findings pave the way to faster and more accurate signal demodulation methods, and testify to the substantial promise of neural network algorithms in signal demodulation problems.

Micrometeoroid and Orbital Debris Impact Detection and Location Based on FBG Sensor Network Using Combined Artificial Neural Network and Mahalanobis Distance Method

Micrometeoroid and orbital debris (MMOD) can cause serious impact damage to long-term on-orbit spacecraft. MMOD impact detection and location are essential to improve the safety and reliability of the on-orbit spacecraft. In this article, a combined artificial neural network (ANN) and Mahalanobis distance method for MMOD impact detection and location based on fiber Bragg grating (FBG) sensor network is proposed. The inputs of the proposed ANN are the Mahalanobis distances determined by the wavelength changes of the FBG sensors at impact points and reference points. A finite element model is developed to explore the relationship between the Mahalanobis distance and the actual distance, which proved to be nonlinear based on the finite element simulation results of MMOD impacts. Four FBG sensors were installed on the back of the test board and sampled at a frequency of 100 Hz using a four-channel FBG demodulator. A total of 360 impact experiments were carried out on this experiment system. The average location error of the proposed method is 0.89 cm, which is 39% and 31% lower compared with the Mahalanobis distance discriminant analysis method and the ANN method, respectively. The combined ANN and Mahalanobis distance method reduces the location errors and has higher accuracy of position prediction.

Fiber Bragg Grating Demodulation Method Based on Improved Manta Ray Foraging Optimization Algorithm

Fiber Bragg Grating Demodulation Method Based on Improved Manta Ray Foraging Optimization Algorithm

FBG Demodulation With Enhanced Performance Based on Optical Fiber Relative Delay Measurement

Fiber Bragg grating (FBG) demodulation based on microwave photonics (MWP) would have a high resolution but the measurement range is usually limited. Here, we propose an FBG demodulation system based on wavelength to optical fiber relative delay (OFRD) mapping, where the signal reflected by the FBG is first modulated by an RF signal and then sent to a dispersion compensation fiber (DCF). The OFRD can be acquired by measuring the slope change of the phase response of the DCF. Thanks to the sub-picosecond resolution OFRD measurement of the phase-derived ranging, the FBG demodulation resolution would be very high. The integer ambiguity, which is the primary limitation for large measurement range, is removed by sweeping the frequency of the RF signal in a certain range. In addition, the relative slope change of the phase response can be obtained using a larger frequency sweep interval compared to the traditional phase-derived ranging method, leading to a significant reduction of the measurement time. An experiment is performed, which applies the system to measure the temperature. A measurement range of more than 300 °C (corresponding to a wavelength shift of 3 nm) and measurement precision of ± 0.2 °C (corresponding to a wavelength shift of 2 pm) are achieved with a measurement speed of 20 ms.

Fast Demodulation of Fiber Bragg Grating Wavelength From Low-Resolution Spectral Measurements Using Buneman Frequency Estimation

This paper presents a fast demodulation algorithm to determine fiber Bragg grating (FBG) from spectra acquired by low wavelength resolution spectrometers. A modified and improved Buneman frequency estimation algorithm is employed in tracking FBG wavelength and eliminating FBG wavelength discontinuities in real-time wavelength demodulation calculations. The FBG demodulation algorithm was tested using a 512-element detector array spectrometer with a 156 pm/element resolution. At a 2-kHz spectrum acquisition rate, the demodulation algorithm achieved a 0.048 pm Bragg wavelength resolution. The computational efficiency and robustness of the new algorithm was compared with a Gaussian FBG peak fitting method. Our method provides a real-time demodulation scheme to track FBG wavelength using low-resolution spectrometers or tunable lasers. It has the potential to significantly reduce the cost of FBG interrogation systems that are employed in high-sensitivity and high-speed sensing applications.

The Correction of Nonlinearity in Wavelength Scanning Based on Long-OPD Interferometer for Fiber Bragg Grating Demodulation in Environment With Variable Temperature

Fabry-Perot (F-P) filter based fiber Bragg grating (FBG) demodulation has been widely applied in many areas. However, the F-P filter suffers from random fluctuation in wavelength scanning when the ambient temperature changes severely, which reduces the demodulation accuracy inevitably. In this paper, a demodulation method based on an etalon and a fiber-optic Michelson interferometer is proposed to improve the demodulation accuracy in an environment with variable temperature. The experiment is carried out by constructing an interferometer with long-optical path difference (OPD). When the environment temperature increases from 20 °C to 60 °C in 30 minutes, the results show that the error of the proposed method is reduced from ± 20.39 pm to ± 6.89 pm. The standard deviation is 3.11 pm, which is reduced by 43.92% compared with the etalon-based demodulation method.