Brillouin Optical Time-domain Analysis System Research Articles

Brillouin expanded time-domain analysis based on dual optical frequency combs

Brillouin Optical Time-Domain Analysis (BOTDA) is a widely-used distributed optical fiber sensing technology employing pulse-modulated pump waves for local information retrieval of the Brillouin gain or loss spectra. The spatial resolution of BOTDA systems is intrinsically linked to pulse duration, so high-resolution measurements demand high electronic bandwidths inversely proportional to the resolution. This paper introduces Brillouin Expanded Time-Domain Analysis (BETDA) as a modified BOTDA system, simultaneously achieving high spatial resolution and low detection bandwidth. Utilizing two optical frequency combs (OFCs) with different frequency intervals as pump and probe, local Brillouin gain spectra are recorded by their spectral beating traces in an expanded time domain. A 2-cm-long hotspot located in a 230 m single-mode fiber is successfully measured in the time domain with a detection bandwidth of less than 100 kHz using dual OFCs with tailored spectral phase, line spacing, and bandwidth.

Performance Enhancement in a Few-Mode Rayleigh-Brillouin Optical Time Domain Analysis System Using Pulse Coding and LMD Algorithm

Rayleigh Brillouin optical time domain analysis (BOTDA) uses the backscattered Rayleigh light generated in the fiber as the probe light, which has a lower detection light intensity compared to the BOTDA technique. As a result, its temperature-sensing technology suffers from a low signal-to-noise ratio (SNR) and severe sensing unreliability due to the influence of the low probe signal and high noise level. The pulse coding and LMD denoising method are applied to enhance the performance of the Brillouin frequency shift detection and temperature measurement. In this study, the mechanism of Rayleigh BOTDA based on a few-mode fiber (FMF) is investigated, the principles of the Golay code and local mean decomposition (LMD) algorithm are analyzed, and the experimental setup of the Rayleigh BOTDA system using an FMF is constructed to analyze the performance of the sensing system. Compared with a single pulse of 50 ns, the 32-bit Golay coding with a pulse width of 10 ns improves the spatial resolution to 1 m. Further enhanced by the LMD algorithm, the SNR and temperature measurement accuracy are increased by 5.5 dB and 1.05 °C, respectively. Finally, a spatial resolution of 1.12 m and a temperature measurement accuracy of 2.85 °C are achieved using a two-mode fiber with a length of 1 km.

Microwave-free BOTDA based on a continuous-wave self-sweeping laser.

This Letter presents the first demonstration, to our knowledge, of a Brillouin optical time domain analysis (BOTDA) system based on a self-sweeping fiber laser. The unique feature of such laser sources is the generation of a narrowband tunable radiation with a small (6 MHz) tuning step and a reasonably large tuning range (3 GHz) making self-sweeping lasers extremely attractive for use in BOTDA systems. Since the wavelength tuning occurs solely due to internal processes occurring in the laser cavity, there is no need to use complex current/temperature control and feedback systems. This makes it possible to completely eliminate microwave devices, such as electro-optic modulators, from the BOTDA design. In this work, distributed temperature measurements with sensing line length of 25 km, spatial resolution of 10 m, and sensitivity of 2°C is demonstrated in a BOTDA system based on an Er-doped self-sweeping laser. The described approaches can reduce the complexity and overall cost of the BOTDA systems.

Cascaded Feedforward Neural Network Based Simultaneously Fast and Precise Multi-Characteristics Extraction and BFS Error Estimation

We present and investigate a scheme to realize fast and precise multi-characteristics extraction and error estimation of Brillouin frequency shift (BFS) simultaneously based on a cascaded feedforward neural network for a Brillouin optical time-domain analyzer (BOTDA) system. The cascaded feedforward neural network is composed of two neural networks in series, where the pre-network is used to extract linewidth (FWHM), signal-to-noise ratio (SNR) and BFS from noised gain spectrum and the post-network utilizes these characteristics and known frequency step to estimate the statistical error of the extracted BFS. The extracted BFS can be used to obtain the environment information along the fiber, while the extracted SNR, FWHM and the estimated BFS error can be utilized to verify the validity of data and monitor the performance of the BOTDA system. The creation process and the performance of the pre-network, post-network and cascaded network are described and analyzed in detail. Both simulation and experimental results show that the cascaded feedforward neural network has higher accuracies on extractions of characteristics (BFS, SNR, FWHM) and error estimation than conventional Lorentzian curve fitting method and error estimation formula when the scanning frequency step of system is large or SNR is low. More importantly, the network is more than 300 times faster than the LCF for simultaneously multi-characteristics extraction and error estimation, which make it possible to real-time monitor the performance of BOTDA system.

Centimeter-level spatial resolution Brillouin optical time domain analyzer using mono-pulse self-difference.

To break through the limitation of pulse width on spatial resolution in the Brillouin optical time-domain analysis system (BOTDA), a novel, to the best of our knowledge, scheme based on mono-pulse self-difference (MPSD) is proposed for centimeter-level spatial resolution. It is performed by the self-difference of the temporal trace generated by a single-shot long pulse in the conventional BOTDA. The concept-proof of the proposed approach is demonstrated, and experimental results are presented where distributed temperature sensing with a 5-cm spatial resolution is realized based on a 40-ns pulse along a 2-km sensing fiber.

Efficient two-stage strain/temperature measurement method for BOTDA system based on Bayesian uncertainty quantification

Brillouin optical time-domain analysis (BOTDA) is an efficient distributed structural strain/temperature measurement technology by identifying the Brillouin frequency (BF) of the fiber. The original BF identification method involves gradually tuning the scanning frequency of the probe laser light, which is time-consuming. To improve the sensing efficiency of BOTDA, a novel two-stage strain/temperature measurement method is proposed based on Bayesian uncertainty quantification. The method uses the information of a small amount of wide range frequency scanning data in the 1st stage to guide a concentrated scanning of the frequency points in the 2nd stage by employing transitional Markov chain Monte Carlo (TMCMC) algorithm. The second-stage scanning strategy based on Bayesian uncertainty quantification ensures the high confidence for BF identification, so that the number of required scanning points can be significantly reduced without sacrificing the measurement accuracy. Finally, the efficacy of the proposed method is verified using numerical and experimental data.

Numerical Investigation of Golay Coding Brillouin Optical Time-Domain Analysis System Based on π-Phase Pulse

Numerical Investigation of Golay Coding Brillouin Optical Time-Domain Analysis System Based on <i>π</i>-Phase Pulse

Dynamic Distributed Pressure Measurement Using Frequency-Agile Fast BOTDA

A distributed pressure sensor is proposed and designed for dynamic pressure measurement by using frequency-agile fast Brillouin optical time-domain analysis (BOTDA). A double coated single-mode fiber (SMF) is used as the sensing fiber because of its 1500- $\mu \text{m}$ thickness outer coating that can significantly improve the dependence of the Brillouin frequency shift on pressure. Then, the frequency-agile technique is used to generate frequency-agile pump pulse in BOTDA system so that a fast Brillouin gain spectrum distribution measurement can be achieved. An injection locked scheme is used as both optical filter and optical amplifier to improve the measurement accuracy. As a result, the static pressure sensitivity of the double coated fiber is −3.46 MHz/MPa with the pressure ranging from 0 to 30 MPa. Subsequently, a large dynamic range pressure measurement is performed with a sampling rate of 33.3 kHz and maximum sensing distance of 25 m. Furthermore, it is proved that the uncertainty of dynamic pressure measurement system is 0.03 MPa.

Review of Specialty Fiber Based Brillouin Optical Time Domain Analysis Technology

Specialty fibers have introduced new functionalities and opportunities in distributed fiber sensing applications. Particularly, Brillouin optical time domain analysis (BOTDA) systems have leveraged the unique features of specialty fibers to achieve performance enhancement in various sensing applications. This paper provides an overview of recent developments of the specialty fibers based BOTDA technologies and their sensing applications. The specialty fibers based BOTDA systems are categorized and reviewed based on the new features or performance enhancements. The prospects of using specialty fibers for BOTDA systems are discussed.

Fast extraction for Brillouin frequency shift in BOTDA system

In this paper, we introduce the basic principle of the Brillouin optical time domain analysis (BOTDA) system and simulate the gain-type BOTDA system. In order to extract the Brillouin frequency shift more accurately, the filtering method is vitally important. In this letter, Gaussian filtering, median filtering and hybrid filtering are used to remove the noise respectively. It is observed that hybrid filtering has the best performance among the three methods, which can improve the signal-to-noise ratio by 9.18 dB. Besides, a peak location method is proposed to quickly extract the center frequency of the filtered Brillouin gain spectrum. This method reduces processing complexity by omitting Lorentz fitting and it possesses the advantages of fast processing rate and high accuracy. Therefore, the peak location method we proposed can be considered as an attractive method for extracting temperature distributions.

Ultrafast and Accurate Temperature Extraction via Kernel Extreme Learning Machine for BOTDA Sensors

Brillouin optical time-domain analyzer (BOTDA) is used to monitor the temperature and strain along a fiber. So far, neural network and machine learning methods have been successfully applied for temperature extraction. But for different frequency scanning steps, different networks should be designed and trained. Here, a BOTDA assisted by kernel extreme learning machine (K-ELM) with high generalization is proposed and experimentally demonstrated. By utilizing K-ELM, the raw Brillouin gain spectra measured from BOTDA system are classified into different temperature classes. The performance of K-ELM is investigated both in simulation and experiment under different cases of signal-to-noise ratios, pump pulse widths, and frequency scanning steps. Compared with curve fitting methods, the K-ELM algorithm has better measurement accuracy of 0.3 °C and it realizes great improvement of the processing speed over 120 times. The ultrafast processing speed, high accuracy and generality make K-ELM become a highly competitive candidate for the high-speed BOTDA sensing system.

Combining Stokes and anti-Stokes interactions to achieve ultra-low noise dynamic Brillouin strain sensing

Brillouin based fiber sensors are susceptible to a range of technical and environmental noise sources that can degrade the sensor performance or introduce unacceptable levels of crosstalk. Here, we introduce a new measurand that combines information from the complex Stokes and anti-Stokes interactions to extract the Brillouin frequency shift while suppressing noise and crosstalk originating from fluctuations in the power, frequency, or polarization state of the pump and probe beams. We then present a modified slope-assisted Brillouin optical time domain analysis (BOTDA) architecture capable of simultaneously recording the gain, loss, and phase associated with both the Stokes and anti-Stokes interactions. We experimentally confirm that the sensor is able to reject noise due to laser frequency jitter, intensity noise, or polarization fading and is immune to crosstalk due to effects such as pump depletion. By suppressing these noise sources, the sensor is able to achieve a minimum detectable strain of only 15.6 nε/Hz1/2 in ∼1 km of fiber with 4 m spatial resolution and a sensor bandwidth of 25 kHz, representing a significant noise improvement compared to state-of-the-art BOTDA systems.

Stimulated Brillouin scattering in a tapered dual-core As2Se3-PMMA fiber for simultaneous temperature and strain sensing.

Chalcogenide fibers are currently being used widely in nonlinear optical signal processing, as they exhibit ultrahigh nonlinearity. Here, we propose a sensor based on stimulated Brillouin scattering for simultaneous temperature and strain measurement in a dual-core tapered As2Se3-polymethyl methacrylate fiber using a Brillouin optical time-domain analysis system. Different Brillouin frequency responses under temperature and strain variations and the separation of Brillouin frequency shifts (BFSs) in two principal polarization axes are demonstrated experimentally over a 50-cm-long tapered dual-core hybrid microfiber. The temperature coefficients are -3.8272MHz/∘C and -3.3302MHz/∘C, and the strain coefficients are -0.06143MHz/µε and -0.03463MHz/µε. Due to the different temperature and strain dependences of Brillouin frequency peaks in two polarizations, temperature and strain resolutions of 1°C and 33µε are realized, respectively. Numerical simulations are also reported to account for the BFS difference in two polarization axes.

BOTDA Fiber Sensor System Based on FPGA Accelerated Support Vector Regression

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.

A Novel Method of the Brillouin Gain Spectrum Recognition Using Enhanced Sobel Operators Based on BOTDA System

Enhanced Sobel operators were proposed to identify the scattering spectrum based on a Brillouin optical time-domain analysis (BOTDA) system and experimentally demonstrated. To the best of our knowledge, this is the first time to use the enhanced Sobel operators in the BOTDA system. The proposed approach regarded the Brillouin scattering spectrum as an image, where the Brillouin gain could be regarded as edges of the image, the Brillouin gain spectrum was sharpened more, and better denoising effect was achieved by the enhanced Sobel operators. Not only that, the novel method could omit the Lorentz fitting and make scattering spectrum recognition more intuitive and fast regardless of fitting multiple points caused by different temperatures or strain along the different positions of the fiber. We successfully realized the recognition method over a 14-km single-mode fiber with 10-m spatial resolution and obtained the temperature at 61.4°C of the heated water, compared with the consequence (61.4°C) of Lorentz curve fitting without much sacrifice of the sensing accuracy.

Deep neural networks assisted BOTDA for simultaneous temperature and strain measurement with enhanced accuracy.

Simultaneous temperature and strain measurement with enhanced accuracy by using Deep Neural Networks (DNN) assisted Brillouin optical time domain analyzer (BOTDA) has been demonstrated. After trained by using combined ideal clean and noisy BGSs, the DNN is applied to extract both the temperature and strain directly from the measured double-peak BGS in large-effective-area fiber (LEAF). Both simulated and experimental data under different temperature and strain conditions have been used to verify the reliability of DNN-based simultaneous temperature and strain measurement, and demonstrate its advantages over BOTDA with the conventional equations solving method. Avoiding the small matrix determinant-induced large error, our DNN approach significantly improves the measurement accuracy. For a 24-km LEAF sensing fiber with a spatial resolution of 2m, the root mean square error (RMSE) and standard deviation (SD) of the measured temperature/strain by using DNN are improved to be 4.2°C/134.2με and 2.4°C/66.2με, respectively, which are much lower than the RMSE of 30.1°C/710.2με and SD of 19.4°C/529.1με for the conventional equations solving method. Moreover, the temperature and strain extraction by DNN from 600,000 BGSs along 24-km LEAF requires only 1.6s, which is much shorter than that of 5656.3s by the conventional equations solving method. The enhanced accuracy and fast processing speed make the DNN approach a practical way of achieving simultaneous temperature and strain measurement by the conventional BOTDA system without adding system complexity.

Subdivision of Brillouin gain spectrum to improve the spatial resolution of a BOTDA system.

A pulse subdivision analysis method was developed to improve the spatial resolution of a conventional long pump pulse Brillouin optical time domain analysis (BOTDA) system. An exclusive photodetector was used to obtain the accurate energy distribution along the long pulse, based on which the long pulse was subdivided into several sub-pulses with certain energy weights. With these energy weights, the Brillouin spectrum generated by the long pulse was subdivided into equal numbers of sub-spectra. Each sub-spectrum could provide detailed sensing information about a fiber sub-segment related to a sub-pulse. Thus, the actual spatial resolution of the BOTDA system was determined by the sub-pulse instead of the long pulse. As a result, spatial resolution was increased by several times, depending on the subdivision multiples. The method was theoretically simulated and experimentally demonstrated. For experimental demonstration, the recognization capability of the melting point of two different fiber sections and the discrete strain distribution on a sensing fiber were respectively tested. For melting point recognization, thanks to five-multiple subdivision, a 1m spatial resolution over 31km sensing fiber was realized using a 50ns pump pulse. For the strain sensing test, ten-multiple subdivision was performed to distinguish two 0.5m stretched fiber sections with a 0.2m interval using a 20ns pump pulse. The spatial resolution is 0.2m, which is a ten times' improvement compared with that before subdivision analysis. Due to its simplicity and cost-effectiveness, the method is believed to have extensive application prospects in distributed fiber sensing fields.

Real-Time Denoising of Brillouin Optical Time Domain Analyzer With High Data Fidelity Using Convolutional Neural Networks

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.

Design rules for optimizing unipolar coded Brillouin optical time-domain analyzers.

The performance of unipolar unicolor coded Brillouin optical time-domain analysis (BOTDA) is evaluated based on both Simplex and Golay codes. Four major detrimental factors that limit the system performance, including decoded-gain trace distortion, coding pulse power non-uniformity, polarization pulling and higher-order non-local effects, are thoroughly investigated. Through theoretical analysis and an experimental validations, solutions and optimal design conditions for unipolar unicolor coded BOTDA are clearly established. First, a logarithmic normalization approach is proposed to resolve the linear accumulated Brillouin amplification without distortion. Then it is found out that Simplex codes are more robust to pulse power non-uniformity compared to Golay codes; whilst the use of a polarization scrambler must be preferred in comparison to a polarization switch to mitigate uncompensated fading induced by polarization pulling in the decoded traces. These optimal conditions enables the sensing performance only limited by higher-order non-local effects. To secure systematic errors below 1.3 MHz on the Brillouin frequency estimation, while simultaneously reaching the maximum signal-to-noise ratio (SNR), a mathematical model is established to trade-off the key parameters in the design, i.e., the single-pulse Brillouin amplification, code length and probe power. It turns out that the optimal SNR performance depends in inverse proportion on the value of maximum single-pulse Brillouin amplification, which is ultimately determined by the spatial resolution. The analysis here presented is expected to serve as a quantitative guideline to design a distortion-free coded BOTDA system operating at maximum SNR.

Narrowing Brillouin gain spectrum for distortion-free Brillouin optical time-domain analyzers

A high-performance Brillouin optical time-domain analysis (BOTDA) is achieved using the Brillouin gain bandwidth reduction technique combined with high injected probe power. In the probe branch, dual-tone probe wave with fixed frequency separation is used for enhancing the probe power to +5 dBm. In the pump branch, a differential π-phase-shift long-pulse width pair is used to narrow the Brillouin gain spectrum. On the basis of high probe power and 2.5-m spatial resolution, the Brillouin gain spectrum can be narrowed to a level of 17 MHz, whereas the Brillouin gain spectrum of conventional single-pulse BOTDA sensor is 51 MHz. As a result, 50-km sensing range with 2.5-m spatial resolution and 1.1-MHz Brillouin frequency shift (BFS) accuracy has been achieved. Meanwhile, the narrowed Brillouin gain spectrum can give rise to sharp rising/falling edge in the BFS profile when the hot spots are introduced, which increases the detection robustness of the small temperature/strain change in the BOTDA system.