Brillouin Optical Time-domain Analysis Sensors Research Articles

Effective Noise Reduction Filters for Precise Temperature Measurement Using Brillouin Distributed Optical Fiber Sensors

This study explores the effectiveness of various noise reduction filters in accurately measuring temperature distributions over a 38.2 km single-mode fiber (SMF). Brillouin optical time-domain analysis (BOTDA) sensor is utilized to gather Brillouin gain spectra (BGSs), which are denoised using bilateral filter (BF), guided filter (GF), adaptive Wiener filter (AWF), non-local means filter (NLMF), average filter (AF) and disc filter (DF). The temperature distributions over the SMF are then determined by applying least-squares curve fitting (LSCF). The study assesses the efficacy of noise reduction filters considering signal-to-noise ratio (SNR), uncertainty in temperature measurement (UTM), experimental spatial resolution (ESR) and signal processing speed (SPS). Among six different filters, NLMF outperforms other filters which can provide SNR improvement of 10.22 dB for which the UTM can be improved by 58.93% without deteriorating the ESR of the sensor. The noise reduction using such filter can also provide 6.2% faster SPS. Therefore, NLMF can be considered as an effective noise reduction filter for the precise temperature measurement using BOTDA sensors. DUJASE Vol. 8 (1) 42-50, 2023 (January)

A method for monitoring the uneven settlement of shield tunnels considering the flattening effect using distributed strain data measured from BOTDA sensors

When investigating the uneven settlement monitoring of shield tunnels, the influence of the flattening effect under longitudinal bending is rarely considered, which leads to inaccurate and incomplete settlement monitoring. To address this issue, a method for monitoring the uneven settlement of shield tunnels that considers the flattening effect is proposed, which is achieved using high-density strain data from distributed Brillouin optical time domain analysis (BOTDA) sensors. The high-density measured strain data is used to combine global settlement with the additional influence of the flattening effect, which includes extrusion and shear effects. This approach allows the trend of uneven settlement to be effectively monitored. Furthermore, a finite element model updating process is implemented in advance to improve the accuracy of soil parameters and minimize calculation errors in practical engineering. To demonstrate the effectiveness of the proposed method, an application example from the Jinan Rail Transit Line 3 was utilized for numerical simulations and practical tests. The results of the application example reveal that the proposed method has a high accuracy, with measurements comparable to the total station-based method. This indicates that the proposed method is an effective and robust settlement monitoring approach and provides a referential basis for further tunnel operations and maintenance.

A Cross-Diagnosis Method for Determining the Structural Condition of a Long-Span Suspension Bridge Based on the Spatial Windows of Distributed Strain Data

Using structural health monitoring (SHM) techniques, Brillouin optical time-domain analysis (BOTDA) sensors can be mounted along the main box girder entire length of a long-span suspension bridge, and the high-density measured points strain monitoring data can be obtained. However, insufficient research has been conducted on accurately diagnosing the structural condition of a long-span suspension bridge by using the abovementioned strain monitoring data. To address this issue, a cross-diagnosis method that determines the structural condition of long-span suspension bridges based on the distributed strain data spatial window is proposed in this study. First, the distributed strain data spatial window based on a long-span suspension bridge structural symmetry is defined. Then, a method that divides the distributed strain data of the bridge main box girder into different spatial windows using mutual information between the strain data from BOTDA sensors is presented. The special symmetry of the environmental temperature effect on the spatial window structural performance is carried out to separate the temperature effect from the strain monitoring data; this process can effectively reduce the interference of ambient temperature on the results of the structural condition diagnosis. Second, using a convolutional neural network, a diagnosis index of the structural condition is generated by using the correlation model between the high-density measured points and the distributed strain data belonging to one whole spatial window. Regarding one spatial window, the proposed diagnosis index can effectively reflect the variation in the distributed strain correlation model caused by the damaged condition of the long-span suspension bridge to achieve cross-diagnosis of the structural condition of the bridge. Finally, the effectiveness of the proposed method is demonstrated through a numerical simulation using strain monitoring data obtained from a real bridge.

Robust Signal Denoising Techniques for Highly Accurate and Spatial Resolution Preserved Brillouin Optical Time Domain Analysis Sensors

The diminution of measurement uncertainty to an acceptable level and the preservation of experimental spatial resolution are highly desirable in many real-world applications of Brillouin optical time domain analysis (BOTDA) sensors. In practical applications of such sensors, the measurement uncertainty essentially relies upon the signal-to-noise ratio (SNR) of the experimental Brillouin gain spectra (BGSs) obtained throughout the sensing fiber. The improvement of such SNR using improper signal denoising techniques alters the experimental spatial resolution of the BOTDA sensors. In this paper, the use of non-local means filter (NLMF) and anisotropic diffusion filter (ADF) is experimentally demonstrated to enhance the uncertainty in the measurement of temperature using BOTDA sensors. For this purpose, the BGSs along a 41 km fiber are collected by averaging several numbers of BOTDA-traces from BOTDA hardware setup. Such BGSs are first denoised by employing NLMF and ADF to improve the measurement SNR. The Brillouin frequency shifts (BFSs) of denoised BGSs are then extracted via curve fitting technique (CFT). The BFS distribution is finally mapped to temperature distribution depending on the known BFS-temperature relationship of the sensing fiber. The robustness of the used filters is analyzed rigorously in terms of SNRs of BGSs, uncertainty in temperature extraction, spatial resolution and runtime in signal processing. The results indicate that the utilization of NLMF and ADF can enhance the SNRs of BGSs up to the maximum of 15.64 dB and 13.53 dB, respectively. Consequently, the uncertainties in the temperature extraction can be reduced up to the maximum of 52.63% and 57.31% for using NLMF and ADF, respectively. Moreover, both NLMF and ADF can preserve the experimental spatial resolution of the BOTDA sensors and include insignificant runtime to CFT. Thus, NLMF and ADF can be considered as robust signal denoising techniques for highly accurate and spatial resolution preserved BOTDA sensors. DUJASE Vol. 7 (2) 31-38, 2022 (July)

A method for convergence monitoring considering the flattening effect in a shield tunnel with BOTDA sensors

The convergence deformation of shield tunnels, which is induced by the combination of the local circumferential load and the flattening effect under longitudinal differential settlement, is difficult to comprehensively monitor with point sensing technology. To address this problem, a method for convergence monitoring considering the flattening effect in shield tunnels is proposed based on distributed Brillouin optical time-domain analysis (BOTDA) technology. First, the convergence deformation under sectional circumferential pressure is explored based on the conjugated beam method; second, the additional influence of the flattening effect, including extrusion and shear effects, on settlement is analyzed using the force method; third, considering the actual circumferential layout of the distributed optical fiber sensors, the multisection convergence is determined from the spatial correlations of the cross-sectional strain monitoring data by means of an artificial neural network (ANN); then, finally, using the time-series monitoring data, the trend of the convergence deformation can be predicted. In this study, both numerical simulation and practical application are carried out to validate the effectiveness of the proposed method, which is compared with the existing method without consideration of the flattening effect. The results indicate that the proposed method enables high accuracy and robustness in convergence monitoring, thus providing a referential basis for further tunnel operation and maintenance.

Enabling Variable High Spatial Resolution Retrieval From a Long Pulse BOTDA Sensor

Spatial resolution (SR) is one of the most important parameters of Brillouin optical time-domain analysis (BOTDA) sensors, which determines the minimum length that a perturbation event can be distinguished. In the field of Internet of Things (IoT), there is an urgent need for sensors with large-scale high-precision sensing capability for scenarios, such as intelligent monitoring of production lines and urban infrastructure. Conventionally, the SR is normally restricted to be longer than 1 m due to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 10$ </tex-math></inline-formula> -ns acoustic lifetime limitation in silica optical fibers. For long-distance smart monitoring systems, the SR is generally on the order of several meters or even worse. However, it does not meet the needs of many applications. Therefore, there is an urgent need to achieve SR in the submeter magnitude. In this work, for the first time to the best of our knowledge, we propose a convolutional neural network (CNN) to process the data of conventional BOTDA sensors, which achieves unprecedented performance improvement that allows to directly retrieve submeter SR from the sensing system that use long pump pulses. By using the simulated Brillouin gain spectrums (BGSs) as the CNN input and the corresponding high SR Brillouin frequency shift (BFS) as the output target, the trained CNN is able to obtain an SR higher than the theoretical value determined by the pump pulse width. In the experiment, the CNN accurately retrieves 0.5-m hotspots from the measured BGS with pump pulses from 20 to 50 ns, and the acquired BFS is in great agreement with 45/40 ns differential pulse-width pair (DPP) measurement results. Compared with the DPP technique, the proposed CNN demonstrates a twofold improvement in BFS uncertainty with only half the measurement time. In addition, by changing the training data sets, the proposed CNN can obtain tunable high SR retrieval based on conventional BOTDA sensors that use long pulses without any requirement of hardware modifications. It is worth mentioning that the proposed method is also applicable to larger pulse widths to retrieve a submeter SR. The proposed data post-processing approach paves the way to enable novel high SR BOTDA sensors, which brings substantial improvement over the state-of-the-art techniques in terms of system complexity, measurement time, reliability, etc.

Total variation denoising and Euclidean distance based distributed temperature monitoring in Brillouin optical time-domain analysis sensors

The performance of Brillouin optical time domain analysis (BOTDA) sensors is largely deteriorated due to the poor signal-to-noise ratio (SNR) of Brillouin gain spectra (BGSs) collected from the BOTDA experiment. The fast monitoring of distributed temperature using BOTDA sensors is also vital for many longdistant applications. To cope with these requirements, this paper proposes total variation denoising (TVD) and Euclidean distance-based pattern recognition (TEPR) for high-performance BOTDA sensors. The performances of TEPR are analyzed explicitly, and rigorous comparisons have been made with traditional nonlinear least squares fitting (NLSF). The experimentally demonstrated results signify that the proposed TEPR can improve the measurement uncertainty by up to ~55% compared to NLSF without worsening the experimental spatial resolution. The signal processing for using TEPR is also ~4 times faster than that for using NLSF. Hence, the proposed technique is an efficient and reliable alternative for the fast and accurate monitoring of distributed temperature in BOTDA sensors. J. Bangladesh Acad. Sci. 46(2); 193-202: December 2022

Ultra-long Brillouin optical time-domain analyzer based on distortion compensating pulse and hybrid lumped–distributed amplification

A Brillouin optical time-domain analysis (BOTDA) scheme based on hybrid amplification, consisting of a distributed Raman amplification and a lumped amplification, provided by remotely pumped Erbium-doped fibers, is proposed and thoroughly studied, enabling longest sensing distance among existing repeaterless techniques. First, the criteria for optimizing the optical powers entering the sensing fiber are defined. Then, the walk-off effect in the forward distributed Raman amplification and associated self-phase modulation are pointed out and analyzed to describe the BOTDA pulse distortion and its impact on the balanced detection used for relative intensity noise mitigation. A compensating pulse approach is proposed to mitigate signal distortion, guaranteeing the safe use of balanced detection in an ultra-long BOTDA sensor. The combination of all approaches mentioned above is validated in two types of fiber loop configurations, with 150 and 200 km real remoteness, both constituting record-high sensing performance in each case.

High spatial resolution fast Brillouin optical time-domain analysis enabled by frequency-agility digital optical frequency comb.

A significant spatial resolution enhancement scheme for digital optical frequency comb (DOFC)-based fast Brillouin optical time-domain analysis (BOTDA) is proposed and experimentally demonstrated by using frequency-agility probes, without sacrificing the frequency resolution. The proposed system ensures high spatial resolution by using short frame duration, meanwhile enabling high frequency resolution retrieval of the Brillouin gain spectrum using frequency interleaving of multiple frequency-agility DOFC probes. Additionally, quadratic phase coding is introduced to release the influence of the high peak to average power ratio of the probes. Eventually, the proposed BOTDA sensor achieves a record 5-m spatial resolution over 10-km fiber with less than 2-MHz frequency uncertainty, and a 1-GHz dynamic measurement range. For proof of concept, 10-Hz vibration sensing is also successfully demonstrated at a 40-Hz sampling rate, showing great potential for fast measurement. It is worth mentioning that a higher spatial resolution can be achieved by using more frequency-agility DOFC probes, albeit at the expense of increasing the measurement time.

Towards fast sensing along ultralong BOTDA: flatness enhancement by utilizing injection-locked dual-bandwidth probe wave.

Brillouin optical time-domain analysis (BOTDA) using distributed Brillouin amplification (DBA) only requires a milliwatt-level pump to achieve a sensing range beyond 100 km, which provides a powerful tool for temperature/strain sensing. However, similar to the majority of other long-range BOTDAs, the state-of-the-art reports require > 1000 times average, severely restricting the sensing speed. The blind area over tens of kilometers caused by the nonuniform Brillouin response and parasitic amplitude modulation (AM) are crucial factors affecting the signal-to-noise ratio (SNR). Here, a comprehensive performance optimization and substantial enhancement for BOTDA sensors was presented by the direct demodulation of an injection-locked dual-bandwidth probe wave. Injection locking (IL) can completely eliminate the impact of AM noise; dual-bandwidth probe enables self-adaptive pulse loss compensation, thereby intensifying the SNR flatness along the ultralong fiber, and direct probe demodulation can overcome nonlocal effects and allows ∼19.7 dB enhancement of probe input power. Therefore, using only 100 times average, ∼148.3 km sensing, and ∼5 m spatial resolution were achieved with < ∼0.8 MHz standard deviation of Brillouin frequency shift (BFS) over a broad range (∼131.7 km). The reduction in averages was more than 10 times that of the reported majority of long-range BOTDAs. Such performances were achieved without using time-consuming or post-processing techniques, such as optical pulse coding and image denoising. Because this approach is compatible with optical chirp chain technique without frequency sweeping, fast acquisition (0.3 s) was also realized, which has the potential for fast sensing at 3.3 Hz along a ∼150 km fiber.

A Dual-Adaptive Denoising Algorithm for Brillouin Optical Time Domain Analysis Sensor

A dual-adaptive denoising algorithm based on complementary ensemble empirical mode decomposition (CEEMD) and particle swarm optimization (PSO) is proposed to improve the signal-to-noise ratio (SNR) of Brillouin optical time domain analysis (BOTDA) sensor. Here, CEEMD is employed to decompose the measurement signal into a series of intrinsic mode functions (IMFs) adaptively and accurately. The spectrum centroid method is applied to quantify objectively the range of denoising. Then PSO threshold algorithm is dedicatedly designed to search for the optimal/sub-optimal denoising threshold for noised IMFs, which can adjust the threshold size adaptively according to the noise level of input signal. The results show that the SNR can be improved by more than 14 dB and the Brillouin frequency shift (BFS) accuracy is optimized by 1.12 MHz in 22.5 km sensing fiber. This algorithm does not need any condition prediction in the process of denoising, which offers an adaptation and high SNR demodulation scheme for BOTDA sensor with only modifying the software.

Enhanced BOTDA Sensors Based on Brillouin Phase Recovery Using Kramers-Kronig Relation

We have proposed and demonstrated an enhanced Brillouin optical time-domain analysis (BOTDA) sensing system with fast measurement speed based on the Brillouin phase recovery using Kramers-Kronig (KK) relation. The non-Lorentz Brillouin gain spectrum (BGS) and Brillouin phase spectrum (BPS) measured by BOTDA are theoretically validated to satisfy KK relation and hence the BPS is digitally recovered by performing Hilbert Transform (HT) to the measured BGS. The recovery of the whole BPS along the 45km fiber only consumes 0.3s. Both simulation and experiment have been carried out to analyze the quality of the recovered BPS under various conditions. By making use of the excellent linearity of the recovered BPS, we accelerate the data processing speed by three times through Linear Fitting (LF) of BPS, without compromise of the sensing accuracy. With implementation of simple HT and LF, this scheme does not need modification of the BOTDA setup and complicated data processing algorithm, which would be a practical way for applications where fast measurement speed is needed, especially in long-distance and high-spatial resolution sensing.

Overcoming EDFA slow transient effect in a Golay-coded BOTDA sensor by a distributed depletion mapping method.

The erbium-doped-fiber-amplifier (EDFA), generally served as a pre-amplifier, could effectively raise the signal-to-noise ratio (SNR) of a Brillouin optical time-domain analysis (BOTDA) sensor. However, it also induces a distortion in the Brillouin gain spectrum and Brillouin frequency shift measurement errors due to the slow transient effect (STE) in the coded-BOTDA. We propose a distributed depletion mapping (DDM) method to overcome such an effect. A continuous light wave with a particular wavelength is injected to map the STE-induced depletion to compensate for the distortion. The proposed scheme is experimentally demonstrated along a 120-km sensing fiber with 2-m spatial resolution. Experimental results show that the conventional tail-alignment (TA) method cannot compensate for the STE over the whole fiber link, while the proposed DDM method compensates for over 7.69-MHz measurement errors.

New BFS Retrieval Technique for Brillouin Optical Time Domain Analysis Sensor System

In this paper, Gaussian smoothing (GS), non-local means (NLM), and Quaternion Wavelet Transform (QWT) have been described in detail. Furthermore, a Brillouin optical time domain analysis (BOTDA) experimental system was built to verify the denoising algorithms. The principal and experimental analyses show that the QWT algorithm is a more efficient image denoising method. The results indicate that the GS algorithm can obtain the highest signal-to-noise ratio (SNR), frequency uncertainty, and Brillouin frequency shift (BFS) accuracy, and can be executed in an imperceptible time, but the GS algorithm has the lowest spatial resolution. After being denoised by using NLM algorithm, although SNR, frequency uncertainty, BFS accuracy, and spatial resolution significantly improved, it takes 40 min to implement the NLM denoising algorithm for a BGS image with 200 × 100,000 points. Processed by the QWT denoising algorithm, although SNR increases to 17.26 dB and frequency uncertainty decreases to 0.24 MHz, a BFS accuracy of only 0.2 MHz can be achieved. Moreover, the spatial resolution is 3 m, which is not affected by the QWT denoising algorithm. It takes less than 32 s to denoise the same raw BGS data. The QWT image denoising technique is suitable for BGS data processing in the BOTDA sensor system.

Damage detection of tunnel based on the high-density cross-sectional curvature obtained using strain data from BOTDA sensors

Fully distributed Brillouin-scattering-based optical sensors—e.g., Brillouin optical time-domain analysis (BOTDA) sensors—appear to be promising supplementary tools for the SHM of actual tunnels. As far as we know, there is little research on detecting the damage of tunnels by using the strain continuous monitoring data obtained from BOTDA sensors. To address this issue, a method based on high-density cross-sectional curvature is proposed for the damage detection of operating tunnel. First, considering the long-distance and large-scale characteristics of actual tunnels, the strain is not sufficiently sensitive to structural damage; thus, the structural strain is converted into the cross-sectional curvature of the tunnel in order to improve this sensitivity. The condition required for converting the strain at the measured points into corresponding measurements of the cross-sectional curvature of the tunnel is discussed, and on this basis, a discriminative approach to the abovementioned conversion is presented. Second, a damage detection index based on high-density cross-sectional curvature is established, and combined with the novel detection approach, the proposed index is implemented to detect the damage of the tunnel. Finally, numerical examples are presented to analyze and discuss the noise resistance performance and structural damage sensitivity of the proposed method. Moreover, the effectiveness of the proposed method is demonstrated using strain monitoring data obtained from an actual tunnel with a box-type structure.

Improving the Spatial Resolution of a BOTDA Sensor Using Deconvolution Algorithm

Spatial resolution improvement from an acquired measurement using long pulse is developed for Brillouin optical time domain analysis (BOTDA) systems based on the total variation deconvolution algorithm. The frequency dependency of Brillouin gain temporal envelope is investigated by simulation, and its impact on the recovered results of deconvolution algorithm is thoroughly analyzed. To implement a reliable deconvolution process, differential pulse-width pair (DPP) technique is utilized to effectively eliminate the systematic BFS distortion stemming from the frequency dependency of temporal envelope. The width of the pulse pairs should be larger than 40 ns as is analyzed theoretically and verified experimentally. It has been demonstrated that the proposed method can realize flexible adjustment of spatial resolution with enhanced signal-to-noise ratio (SNR) from an established measurement with long pump pulse. In the experiment, the spatial resolution is increased to 0.5 m and 1 m with high measurement accuracy by using the deconvolution algorithm from the measurement of 60/40 ns DPP signals. Compared with the raw DPP results with the same spatial resolution, 9.2 dB and 8.4 dB SNR improvements are obtained for 0.5 m and 1 m spatial resolution respectively, thanks to the denoising capability of the total variation deconvolution algorithm. The impact of sampling rate on the recovery results is also studied. The proposed sensing system allows for distortion-free Brillouin distributed sensing with higher spatial resolution and enhanced SNR from the conventional DPP setup with long pulse pairs.

Long-distance vector Brillouin optical time-domain analysis sensors using distributed Brillouin amplification with frequency-comb pump parallel demodulation

We demonstrated a structure for extending repeater-less sensing distance of Brillouin optical time-domain analysis enhanced by distributed Brillouin amplification (DBA-BOTDA) through a combination with the Brillouin phase spectrum (BPS), where parallel demodulation of the frequency-comb pump can be realized. The careful structure design is implemented to perfectly suppress the impacts of various phase noises on long-distance BPS extraction. ∼98.9 km sensing distance and ∼6.5 m spatial resolution were demonstrated, with only four times frequency sweeping. The standard deviation of Brillouin frequency shift can be decreased to within ∼2 MHz by a combination of Brillouin gain spectrum and BPS.

Effects of Pump-Pulse Width and Temperature on Experimental Brillouin Gain Spectrum Obtained from Brillouin Optical Time-Domain Analysis Sensors

In this paper, the characteristics of Brillouin gain spectrum (BGS) obtained from a Brillouin optical time-domain analysis (BOTDA) sensor are investigated and analyzed experimentally. The measured BGSs obtained for various pump-pulse widths and temperatures are fitted with different spectrum profiles using nonlinear least-squares curve fitting technique. The fitting performances of used profiles are presented and analyzed. Based on such performances, the proper spectrum profile to be used in the fitting process is determined and used to extract key parameters of the measured BGSs accurately. The variations of such key parameters with pump-pulse widths and temperatures are also investigated and analyzed. The results reveal that pump-pulse widths and temperatures have significant effects on the extracted key parameters of the measured BGSs obtained from BOTDA sensors.&#x0D; Bangladesh Journal of Physics, 27(1), 69-80, June 2020

Fast Brillouin optical time-domain analysis using frequency-agile and compressed sensing.

A fast Brillouin optical time-domain analysis (BOTDA) sensor has been proposed and experimentally demonstrated based on the frequency-agile and compressed-sensing technique. The proposed scheme employs a data-adaptive sparse base obtained by the principle component analysis algorithm, enabling the sparse representation of Brillouin spectrum. Then, it can be reconstructed successfully with random frequency sampling and orthogonal matching-pursuit algorithms. In the experiment, the Brillouin gain spectrum (BGS) is mapped by the conventional fast BOTDA, where the frequency step and span are 4 MHz and 500 MHz, respectively. By using compressed-sensing technology, the BGS is successfully recovered with 37 random frequency samples, the number of which is only 30% of the full data. With fewer sampling frequencies, the compressed-sensing technology is able to improve the sensing performance of the conventional fast BOTDA, including a 3.3-time increase in sampling rate and 70% reduction in data storage.

Brillouin optical time-domain analysis enhanced by forward stimulated Brillouin scattering: proof-of-concept demonstration

A distributed Brillouin optical time-domain analysis (BOTDA) sensor enhanced by forward stimulated Brillouin scattering (FSBS) based distributed Brillouin amplification (DBA) was proposed and presented. Due to co-propagating of pump and Stokes lights, the Stokes pulse is effectively amplified in front segment of fiber, resulting in flatter gain distribution. As a proof-of-concept, FSBS-DBA based BOTDA sensing with distance of ∼74.2 km, Brillouin frequency shift standard deviation of less than ∼2 MHz and spatial resolution of ∼10 m was demonstrated. This method is compatible and complementary with backward SBS-based DBA, and features better pumping efficiency than Raman amplification.