Application of remote power-by-light switching in a simplified BOTDA sensor network.

A Brillouin optical time-domain analysis (BOTDA) sensor that combines the conventional complementary coding with the pulse prepump technique for high-accuracy and long-range distributed sensing is implemented and analyzed. The employment of the complementary coding provides an enhanced signal-to-noise ratio (SNR) of the sensing system and an extended sensing distance, and the measurement time is also reduced compared with a BOTDA sensor using linear coding. The combination of pulse prepump technique enables the establishment of a preactivated acoustic field in each pump pulse of the complementary codeword, which ensures measurements of high spatial resolution and high frequency accuracy. The feasibility of the prepumped complementary coding is analyzed theoretically and experimentally. The experiments are carried out beyond 50-km single-mode fiber, and experimental results show the capabilities of the proposed scheme to achieve 1-m spatial resolution with temperature and strain resolutions equal to ∼1.6°C and ∼32 μϵ, and 2-m spatial resolution with temperature and strain resolutions equal to ∼0.3°C and ∼6 μϵ, respectively. A longer sensing distance with the same spatial resolution and measurement accuracy can be achieved through increasing the code length of the prepumped complementary code.

We propose and experimentally demonstrate a Brillouin Optical Time Domain Analysis sensor combining adaptive linear prediction with cyclic pulse coding for fast and accurate strain measurement over 10km standard singlemode fiber with meter-scale spatial resolution.

Frequency-comb-based Brillouin optical time-domain analysis (BOTDA) sensors were developed to achieve acquisition-time reduction and high-spatial-resolution/long-distance sensing simultaneously. We found that, for the standard frequency-comb-based BOTDA, the use of a double-sideband (DSB) pulse generates a series of pulse pairs that simultaneously propagate along the sensing fiber, leading to a nonlinear interaction between the two sidebands of each frequency comb pulse, and a significant splitting of the Brillouin gain spectrum (BGS). This problem prevents its application in high-spatial-resolution sensing due to the higher pulse power requirement. Thus, one of the sidebands of DSB pulse was proposed for greatly suppressing the BGS distortion. In combination with the phonon pre-excitation technique based on phase-shifted pulse, a sensor with a spatial-resolution approximately 60 cm along a fiber approximately 592 m in length was demonstrated. Furthermore, we explored the detailed performance of long-distance sensing by frequency- comb-based BOTDA. The use of a frequency comb for the probe wave can suppress the pulse distortion and non-local effect, which is helpful for extending the sensing distance. A spatial resolution of approximately 6 m along a sensing fiber approximately 74.2 km in length was successfully demonstrated.

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)

We have used distributed Raman amplification to extend the measurement distance of a Brillouin Optical Time-Domain Analysis (BOTDA) sensor. We successfully demonstrate a dynamic range of 75 km with 2 meter spatial resolution.

This paper investigated the evaluation of the concrete damage degree due to steel bar corrosion for reinforced concrete structures. Brillouin optical fiber time domain analysis (BOTDA) sensors were developed to monitor the steel bar corrosion expansion strain. Electrochemical accelerating experimental results showed the sensors could be used for early detection and the lifelong monitoring. The damage factor was proposed to quantitatively evaluate the concrete damage degree before initial cracking and during the development of cracks. Finite element analysis was performed on concrete specimens to map the monitoring results with the damage factor, which supported the capability of the damage factor.

In recent years, many conventional image denoising techniques have been intensively studied to enhance the signal to noise ratio (SNR) of Brillouin optical time domain analyzer (BOTDA), due to their superior denoising performance to one-dimensional methods. However, in the case of low sampling rate, the details of the signal are smoothed out due to less useful information, resulting in a degradation of the spatial resolution. Moreover, these conventional denoising algorithms are quite time-consuming compared with the BOTDA measuring time. To overcome these drawbacks, we employ a feed-forward convolutional neural networks (CNN) based image denoising for BOTDA. A conventional BOTDA system with 15 ns pulse width is implemented to demonstrate the effectiveness of the exploited CNN-based denoising method. The actual electrical noise signals of the BOTDA at different sampling rates are collected to synthesize training samples. The CNN model is trained with the noise and simulated BOTDA signals. Experimental results show that SNR improvement of 13.43 dB, 13.57 dB, and 12.9 dB is achieved at a sampling rate of 500 MSa/s, 250 MSa/s, and 125 MSa/s, respectively, via the trained CNN denoiser. No spatial resolution distortion can be observed in the denoised BOTDA signals. Besides, the CNN denoiser only takes 0.045 s to process a 151 × 50000 image benefiting from GPU computing. This processing time is negligible compared with the acquisition time of BOTDA, which makes real-time denoising possible.

Distributed fiber-optic sensors employ the conventional fiber as the sensing medium and allow all locations along the optical fiber to be measured simultaneously. Thus the sensing scheme provides an efficient way of simultaneously achieving multi-points sensing with a convenient and low-cost configuration. One of the most popular technologies that enables distributed sensing is based on Brillouin scattering. Characteristics of Brillouin scattering are determined mostly by the material properties of the optical fiber and the Brillouin Frequency Shift (BFS) has linear dependence on both the temperature and strain of the fiber under test (FUT). Based on this principle, intensive research efforts have been made in distributed Brillouin fiber sensors during the past two decades. Among them, Brillouin Optical Time-Domain Analysis (BOTDA) has attracted intensive research interest due to its promising properties for temperature and strain monitoring. In BOTDA the local Brillouin Gain Spectrum (BGS) is reconstructed by scanning the frequency offset of a continuous wave and an optical pulse around BFS when they are counter-propagating inside FUT, and the local BFS and hence the temperature and/ or strain information are retrieved accordingly. Usually curve fitting techniques are employed in determining the BFSs of the measured BGSs along the FUT, in which the measured BGS is fitted using an ideal curve and the BFS is found to be the frequency with the peak gain on the fitted curve. Fitting techniques using different lineshape for ideal curves, e.g. Lorentzian curve fitting (LCF), Gaussian curve fitting (GCF), pseudo-Voigt curve fitting (pVCF), and quadratic curve fitting (QCF) have been used to find the BFS distribution along FUT. However, the curve fitting techniques require careful initialization of model parameters and as many data points as possible collected on the measured BGS to ensure the fitting accuracy, otherwise the accuracy in BFS determination will degrade significantly. In addition, long processing time is needed for the iteration of fitting algorithms to find the BFS, especially when the sensing distance is long with a large number of BGSs collected and processed. Recently we have utilized the nonlinear mapping capability of Artificial Neural Network (ANN) in a BOTDA system and has successfully extracted temperature distribution from the measured BGSs along the FUT. Before temperature extraction ANN is trained to acquire the knowledge about the BGS patterns under different temperatures, thus it allows better accuracy even if the data points on the collected BGS become fewer when large frequency scanning step is adopted during the acquisition of BGSs. Moreover, the processing speed of ANN for temperature extraction is much faster than that of curving fitting techniques. In this presentation, we will review our work on using ANN for temperature extraction in a BOTDA system. Different idea spectrum profiles are used for training ANN, and the corresponding performances are compared with those using conventional curve fitting techniques. Some potential applications using ANN in BOTDA are also discussed. We believe that ANN can be an attractive tool for direct temperature or strain extraction in BOTDA system at high accuracy and fast speed.

Brillouin optical time domain analyzer (BOTDA) fiber sensors have shown strong capability in static long haul distributed temperature/strain sensing. However, in applications such as structural health monitoring and leakage detection, real-time measurement is quite necessary. The measurement time of temperature/strain in a BOTDA system includes data acquisition time and post-processing time. In this work, we propose to use hardware accelerated support vector regression (SVR) for the post-processing of the collected BOTDA data. Ideal Lorentzian curves under different temperatures with different linewidths are used to train the SVR model to determine the linear SVR decision function. The performance of SVR is evaluated under different signal-to-noise ratios (SNRs) experimentally. After the model coefficients are determined, algorithm-specific hardware accelerators based on field programmable gate arrays (FPGAs) are used to realize SVR decision function. During the implementation, hardware optimization techniques based on loop dependence analysis and batch processing are proposed to reduce the execution latency. Our FPGA implementations can achieve up to 42x speedup compared with software implementation on an i7-5960x computer. The post-processing time for 96,100 BGSs along 38.44-km FUT is only 0.46 seconds with FPGA board ZCU104, making the post-processing time no longer a limiting factor for dynamic sensing. Moreover, the energy efficiency of our FPGA implementation can reach up to 226.1x higher than software implementation based on CPU.

The sudden collapse of sinkholes in the Dead Sea area represents a serious threat to infrastructure in the area. The formation of these sinkholes has been shown to be directly correlated with the drop in the Dead Sea water level which is accompanied by a corresponding lowering of the groundwater level and permits the penetration of low-salinity groundwater into coastal areas. This water causes dissolution of the salt layers which results in the formation of subsurface voids that develop into collapse sinkholes. Various tools and measurement methods have been investigated in order to attempt to detect the formation of sinkholes, but to date, there is no method capable of providing early warning of possible collapse. This paper investigates the use of fiber-optic Brillouin optical time-domain reflectometry (BOTDR) or Brillouin optical time-domain analysis (BOTDA) for such detection. Brillouin optical time-domain reflectometry or analysis (BOTDR/A) is an optical measurement technique that provides di...

A self-heterodyne detection Rayleigh Brillouin optical time domain analysis system

We propose and experimentally demonstrate the use of principal component analysis (PCA) based pattern recognition to extract temperature distribution from the measured Brillouin gain spectra (BGSs) along the fiber under test (FUT) obtained by Brillouin optical time domain analysis (BOTDA) system. The proposed scheme employs a reference database consisting of relevant ideal BGSs with known temperature attributes. PCA is then applied to the BGSs in the reference database as well as to the measured BGSs so as to reduce their size by extracting their most significant features. Now, for each feature vector of the measured BGS, we determine its best match in the reference database comprised of numerous reduced-size feature vectors of the ideal BGSs. The known temperature attribute corresponding to the best-matched BGS in the reference database is then taken as the extracted temperature of the measured BGS. We analyzed the performance of PCA-based pattern recognition algorithm in detail and compared it with that of curve fitting method. The experimental results validate that the proposed technique can provide better accuracy, faster processing speed and larger noise tolerance for the measured BGSs. Therefore, the proposed PCA-based pattern recognition algorithm can be considered as an attractive method for extracting temperature distributions along the fiber in BOTDA sensors.

Given the strong polarization sensitivity of Stimulated Brillouin Scattering (SBS), in Brillouin Optical Time Domain Analysis (BOTDA) it turns out to be indispensable to perform some kind of polarization scrambling, either in the pump pulse, or the probe signal (or both). This is usually accomplished using polarization scrambling/switching systems, which, being mechanical, tend to be not as robust as it would be desirable. In this paper we propose a completely passive system, with no moving parts, to perform the polarization scrambling in a BOTDA. It is based on the use of balanced detection among the orthogonally polarized Stokes and anti-Stokes bands of the probe signal. The setup requires no alignment and provides a performance similar to a conventional BOTDA sensor.

We experimentally demonstrate the use of artificial neural network (ANN) to process sensing signals obtained from Brillouin optical time domain analyzer (BOTDA). The distributed temperature information is extracted directly from the local Brillouin gain spectra (BGSs) along the fiber under test without the process of determination of Brillouin frequency shift (BFS) and hence conversion from BFS to temperature. Unlike our previous work for short sensing distance where ANN is trained by measured BGSs, here we employ ideal BGSs with different linewidths to train the ANN in order to take the linewidth variation due to different conditions from the training and testing phases into account, making it feasible for long distance sensing. Moreover, the performance of ANN is compared with other two techniques, Lorentzian curve fitting and cross-correlation method, and our results show that ANN has higher accuracy and larger tolerance to measurement error, especially at large frequency scanning step. We also show that the temperature extraction from BOTDA measurements employing ANN is significantly faster than the other two approaches. Hence ANN can be an excellent alternative tool to process BGSs measured by BOTDA and obtain temperature distribution along the fiber, especially when large frequency scanning step is adopted to significantly reduce the measurement time but without sacrifice of sensing accuracy.

In this paper, we have characterised Brillouin frequency shift (BFS) in single mode silica fibre. In particular, BFS was characterised in conventional Brillouin Optical Time Domain Analysis (BOTDA) at operating wavelength of 1550 nm by a pump-probe technique. In addition, we also presented Brillouin gain and Brillouin loss mechanism in BOTDA sensing system. The simulation results shows, BFS have strong linear relationship with acoustic velocity and applied strain along the sensing fibre. A frequency shift of 60 MHz was obtained for each 0.1% strain (1000 μ-strain) on fibre. From the measurement results, it was evident that the each μ-strain on the fibre the frequency shift was found as 0.06 MHz. Therefore, BOTDA sensing system based on BFS is a promoting technique for structural health monitoring.