High-precision and one-end accessible brillouin optical time domain analysis system using round-tripped phase-modulated probe light

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

We proposed a method based on the Brillouin optical time domain analysis (BOTDA) system to demodulate the length changes of the heating region using the intensity of probe light, under the premise that the temperature in the heating region does not change and the Brillouin frequency shift (BFS) of the heating region is known. This method can realize the recognition of small length under the condition of wide pulse width. The theoretical analysis shows that the different lengths of the heating region will lead to different gains when the spatial resolution corresponding to the pulse width of the pulsed light is larger than the length of the heating region. And through theoretical derivation and simulation, it is concluded that the relationship between the intensity of probe light and the length of the heating region satisfies the exponential growth. Further experiments verify that the exponential growth is approximately linear in the range of small length changes. The length of the heating region can be inversely deduced by using the linear relationship by detecting the intensity of probe light. When the length of sensing fiber is 100 m and the heating region changes from 2 cm to 8 cm, 1 cm heating region changes can be identified.

A Brillouin optical time domain analysis (BOTDA) system utilizing tailored compensation for the propagation loss of the pump pulse is demonstrated for long-range and high-resolution distributed sensing. A continuous pump wave for distributed Brillouin amplification (DBA pump) of the pump pulse co-propagates with the probe wave, where gradual variation of the spectral width is additionally introduced to the DBA pump to obtain a uniform Brillouin gain along the position. In the experimental confirmation, a distributed strain measurement along a 51.2 km fiber under test is presented with a spatial resolution of 20 cm, in which the measurement error (σ) of less than 1.45 MHz and the near-constant Brillouin gain of the probe wave are maintained throughout the fiber.

This paper presents reported machine learning approaches in the field of Brillouin distributed fiber optic sensors (DFOSs). The increasing popularity of Brillouin DFOSs stems from their capability to continuously monitor temperature and strain along kilometer-long optical fibers, rendering them attractive for industrial applications, such as the structural health monitoring of large civil infrastructures and pipelines. In recent years, machine learning has been integrated into the Brillouin DFOS signal processing, resulting in fast and enhanced temperature, strain, and humidity measurements without increasing the system’s cost. Machine learning has also contributed to enhanced spatial resolution in Brillouin optical time domain analysis (BOTDA) systems and shorter measurement times in Brillouin optical frequency domain analysis (BOFDA) systems. This paper provides an overview of the applied machine learning methodologies in Brillouin DFOSs, as well as future perspectives in this area.

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.

Brillouin optical time domain analysis (BOTDA) system is a very popular system for distributed optical sensing. In order to achieve its full performance quality, every system element and setting has to be carefully set. This paper specifically performs optimization analysis on the polarization and probe power in measurement of Brillouin gain spectrum and Brillouin frequency shift. The results reveal the necessity of adopting polarization scrambler into the system, and astutely choosing the correct pump power in order to reduce fluctuations in the Brillouin signals.

We propose and demonstrate a novel differential pulse-width pair Brillouin optical time domain analysis (DPP-BOTDA) system with a pulsed-probe wave for online distributed strain measurement of fiber Michelson hydrophones (FMHs). Different from the conventional DPP-BOTDA using a continuous probe wave, a pulsed-probe wave is used in our scheme to avoid the interferences between the reflected lights from the sensor arm and reference arm of the FMH, where the probe pulse width should be adjusting precisely equal to the time delay between the two arms. The Brillouin frequency shift (BFS) containing the strain and/or temperature information is measured by sweeping the frequency difference of the probe pulse and the pump pulse. In the experiment, an optimized 8/8.5 ns pump pulse pair is applied to realize a 5-cm spatial resolution, and the probe pulses of 624.5 ns and 1269 ns are applied to measure the strain distribution of the sensor arms of two FMHs. We have successfully measured the temperature-induced strain of a FMH with an arm length difference of 62.45 m as temperature increases from -40°C to 80°C and the distributed strain variation of the other FMH with the arm length difference of 126.91 m as the hydrostatic pressure increases from 0.1 MPa to 10 MPa. The results indicate that the proposed scheme can provide a desirable solution for online distributed strain measurement of the FMHs.

This research proposes the shape reconstruction algorithm for the deformation monitoring of composite structures using the high-resolution distributed strain data, which is obtained by the embedded optical fiber network. The accurate shape monitoring system is expected to be useful for full-scaled structural monitoring of larger composite structures. Once the optical fiber network is installed in the structure, such global shape monitoring system will provide an excellent tool for many monitoring applications in their life time. In this paper, we constructed the reconstruction algorithm for the deflection in the bending deformation of composite laminates. The high-resolution distributed strain data was obtained by one of the optical fiber sensing systems, pulse-pre-pump Brillouin optical time domain analysis (PPP-BOTDA) system. We fabricated a composite laminate specimen with an embedded optical fiber network, and the cantilever bending test was carried out. Using obtained distributed data, the deflection of the specimen was predicted using the constructed algorithm. The deflections were successfully reconstructed with a few percents of the prediction error. From the result, it was shown that the deformation of composite structures was able to be reconstructed with high estimation accuracy using our algorithm, which uses distributed strain data obtained by the embedded optical fiber network.

The dependence of Brillouin linewidth and peak frequency on lightwave state of polarization (SOP) due to fiber inhomogeneity in single mode fiber (SMF) is investigated by using Brillouin optical time domain analysis (BOTDA) system. Theoretical analysis shows fiber inhomogeneity leads to fiber birefringence and sound velocity variation, both of which can cause the broadening and asymmetry of the Brillouin gain spectrum (BGS) and thus contribute to the variation of Brillouin linewidth and peak frequency with lightwave SOP. Due to fiber inhomogeneity both in lateral profile and longitudinal direction, the measured BGS is the superposition of several spectrum components with different peak frequencies within the interaction length. When pump or probe SOP changes, both the peak Brillouin gain and the overlapping area of the optical and acoustic mode profile that determine the peak efficiency of each spectrum component vary within the interaction length, which further changes the linewidth and peak frequency of the superimposed BGS. The SOP dependence of Brillouin linewidth and peak frequency was experimentally demonstrated and quantified by measuring the spectrum asymmetric factor and fitting obtained effective peak frequency respectively via BOTDA system on standard step-index SMF-28 fiber. Experimental results show that on this fiber the Brillouin spectrum asymmetric factor and effective peak frequency vary in the range of 2% and 0.06MHz respectively over distance with orthogonal probe input SOPs. Experimental results also show that in distributed fiber Brillouin sensing, polarization scrambler (PS) can be used to reduce the SOP dependence of Brillouin linewidth and peak frequency caused by fiber inhomogeneity in lateral profile, however it maintains the effects caused by fiber inhomogeneity in longitudinal direction. In the case of non-ideal polarization scrambling using practical PS, the fluctuation of effective Brillouin peak frequency caused by fiber inhomogeneity provides another limit of sensing frequency resolution of distributed fiber Brillouin sensor.

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.

The Pound-Drever-Hall (PDH) technique is a powerful method for laser frequency stabilization, and a PDH loop can also act as a single-channel sensor with high resolution and fast response speed. In this work, we expand the PDH technique into a time-domain distributed form, accomplishing a fast Brillouin optical time domain analysis (BOTDA) scheme which measures the distribution of Brillouin frequency shifts along optical fiber without frequency scanning. Approaches of phase modulated probe light, synchronous demodulation for PDH error, arbitrarily accessing in single interrogation, and distributed form feedback control are employed in the system. The scheme achieves fast measurement speed and large dynamic range at the same time, as well as good response linearity and high tolerance to optical power fluctuation. In the demonstration experiment, sensing resolution of 0.4 MHz and measurement range of 300 MHz are realized, under the condition of 25 km single mode communication fiber, 5 m spatial resolution and 0.24 s measurement time (400 times of averaging).

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.

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...

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.