Time Domain Reflectometry System Research Articles

Distributed Acoustic Sensing Signal Model Under Static Fiber Conditions

This paper presents a statistical model for distributed acoustic sensor interrogation units that utilizes laser pulses transmitted into fiber optics. Interactions within the fiber lead to localized acoustic energy, resulting in backscatter, which is a reflection of the light. Explicit equations were used to calculate the amplitudes and phases of backscattered signals. The proposed model accurately predicts the amplitude signal spectrum and autocorrelation, aligning well with experimental observations. This study also explores the phase signal characteristics relevant to optical time-domain reflectometry (OTDR) system sensing applications, demonstrating consistency with the experimental results. The experiments were conducted using Python coding, enabling the analysis of the individual components of the Distributed Acoustic Sensing (DAS) system. The assumptions of the model include the static condition of the fiber, implying the absence of external forces or vibrations. Consequently, no external acoustic disturbances were considered. The backscattered signal comprises a random noise component resulting from intrinsic fiber imperfections, and a coherent component arising from the interplay between the laser pulse and the fiber. Keywords: distributed acoustic sensing, fiber optics, optical time-domain reflectometry, Rayleigh scattering.

Curing process monitoring of cementitious grout using time domain reflectometry (TDR) system

Curing process monitoring of the cementitious grout is necessary for its successful use and to ensure the serviceability of the constructed, repaired, and stabilized infrastructures using that material. In this study, time domain reflectometry (TDR) system is applied to monitor the curing process of the cementitious grout based on the changes in relative permittivity (εr). To utilize the TDR system to electrically conductive cementitious grout, a TDR probe with coated core electrode (coated probe) is manufactured, and a calibration is performed to evaluate the formal εr by using the coated probe. The coated probes are installed in the middle and at the bottom of the cementitious grouts with a length of 950 mm, and the εr have been evaluated with curing time using both middle probe (εr(middle)) and bottom probe (εr(bottom)). In addition, compressive strength tests are performed on separately prepared specimens for comparative analyses. Experimental results show that the εr(middle) and εr(bottom) decrease with curing time and converge from the fully cured time, and the curing process of cementitious grout monitored by the TDR system is consistent with the compressive strength developments of the specimens tested with curing time. Therefore, the TDR system with a coated probe may effectively be used for monitoring the curing process of the cementitious grouts based on the changes in electromagnetic property.

Water Pipeline Leakage Detection Based on Coherent φ-OTDR and Deep Learning Technology

Leakage in water supply pipelines remains a significant challenge. It leads to resource and economic waste. Researchers have developed several leak detection methods, including the use of embedded sensors and pressure prediction. The former approach involves pre-installing detectors inside pipelines to detect leaks. This method allows for the precise localization of leak points. The stability is compromised because of the wireless signal strength. The latter approach, which relies on pressure measurements to predict leak events, does not achieve precise leak point localization. To address these challenges, in this paper, a coherent optical time-domain reflectometry (φ-OTDR) system is employed to capture vibration signal phase information. Subsequently, two pre-trained neural network models based on CNN and Resnet18 are responsible for processing this information to accurately identify vibration events. In an experimental setup simulating water pipelines, phase information from both leaking and non-leaking pipe segments is collected. Using this dataset, classical CNN and ResNet18 models are trained, achieving accuracy rates of 99.7% and 99.5%, respectively. The multi-leakage point experiment results indicate that the Resnet18 model has better generalization compared to the CNN model. The proposed solution enables long-distance water-pipeline precise leak point localization and accurate vibration event identification.

Signal-to-Noise Ratio Improvement for Phase-Sensitive Optical Time-Domain Reflectometry Using a Genetic Least Mean Square Method

In this paper, a genetic least mean square (GLMS) method is proposed to improve the signal-to-noise ratio (SNR) of acoustic signal reconstruction in a phase-sensitive optical time-domain reflectometry system. The raw demodulated signal is processed via applying the least mean square criterion. The SNR of the processed signal was calculated and served as the objective function in the fitness evaluation procedure. The genetic operations of the population selection, crossover, and mutation are sequentially performed and repeated until the suspensive condition is reached. Through multiple iterations, the GLMS method continuously optimized the population to find the optimal solution. Experimental results demonstrate that the SNR is substantially improved by 14.37–23.60 dB in the monotonic scale audio signal test from 60 to 1000 Hz. Furthermore, the improvement of the phase reconstruction of a human voice audio signal is also validated by exploiting the proposed GLMS method.

Improving BFS measurement accuracy of BOTDR based on Cauchy proximal splitting

A novel approach based on the Cauchy proximal splitting (CPS) algorithm is proposed to improve Brillouin frequency shift (BFS) measurement accuracy in Brillouin optical time domain reflectometry system. The CPS algorithm utilizes proximal splitting to handle the data and the penalty function based on Cauchy distribution to accurately estimate the target signal to promote sparsity, achieving significant improvement in signal-to-noise ratio (SNR). Experimental results confirm that with a SNR increase of 12.7 dB, an increase in BFS measurement accuracy from 4.78 MHz to 0.43 MHz is achieved over a 10.3 km sensing fiber. The denoising effects of the CPS, wavelet denoising (WD) and non-local mean (NLM) algorithms are further compared, demonstrating that the CPS algorithm has the lowest root mean square error (0.43 MHz) and no deterioration in spatial resolution rather than the WD and NLM algorithms does.

Vibration Event Recognition Using SST-Based Φ-OTDR System.

We propose a method based on Synchrosqueezing Transform (SST) for vibration event analysis and identification in Phase Sensitive Optical Time-Domain Reflectometry (Φ-OTDR) systems. SST has high time-frequency resolution and phase information, which can distinguish and enhance different vibration events. We use six tap events with different intensities and six other events as experimental data and test the effect of attenuation. We use Visual Geometry Group (VGG), Vision Transformer (ViT), and Residual Network (ResNet) as deep classifiers for the SST transformed data. The results show that our method outperforms the methods based on Continuous Wavelet Transform (CWT) and Short-Time Fourier Transform (STFT), while ResNet is the best classifier. Our method can achieve high recognition rate under different signal strengths, event types, and attenuation levels, which shows its value for Φ-OTDR system.

Comparison of Amplitude-Frequency Response Characteristics Between DCM and Arctan Algorithms in ϕ-OTDR

In phase-sensitive optical time-domain reflectometry (ϕ-OTDR) systems, DCM and Arctan demodulation algorithms are typically used to recover the information on vibration events along the fiber. The amplitude-frequency response characteristics of the DCM and Arctan algorithms are investigated and compared using a 3×3 coupler demodulation technique. We first testify the demodulation capability of the DCM and Arctan algorithms through experiments in ϕ-OTDR. Then the amplitude-frequency response ranges of the two algorithms are analyzed according to the demodulated phase error distribution from both the experiment and simulation. It is shown that the Arctan algorithm has a wider amplitude-frequency range than the DCM algorithm. The phase error generation mechanism is also investigated and we find that, in a noise-free condition, the phase errors still appear with use of the DCM algorithm, which cannot be explained by the general perspective that the phase errors arise from the sensitivity of the differential operator to noise. For both algorithms the phase error is related to the amplitude (D) and frequency (f) of the vibration signal, and pulse repetition frequency <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(f_pulse)$</tex-math></inline-formula> . When the phase error tolerance is less than 0.1, the expression of the amplitude-frequency response characteristic of DCM algorithm is 5.7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(D+1)f\leq f_pulse$</tex-math></inline-formula> , whereas that of the Arctan algorithm is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2Df\leq f_pulse$</tex-math></inline-formula> .

Water hammer detection based on FIV online analysis using a distributed fiber optic sensor.

In long-distance oil pipelines, the abnormal flow state of the fluid in the pipeline can cause continuous water hammer effects, which can damage the pipeline and even cause leakage or collapse. Based on this phenomenon, this paper proposes a new, to the best of our knowledge, detection method based on sensing optical fiber and phase-sensitive optical time-domain reflectometry (φ-OTDR) system to solve the problem of continuous water hammer detection in long-distance pipelines. The method uses a non-invasive, low-cost, real-time approach to monitor pipeline wall deformation characteristics and assess flow-induced vibration (FIV) intensity by statistically distinguishing water hammer signals from normal vibration signals and calculating the peak-to-average ratio (PAR) of these signals. Experimental results show that the FIV status of different types of pipelines can be effectively monitored by calculating the PAR of water hammer signals and vibration signals. The measured PAR based on the φ-OTDR system has high consistency with the PAR variation trend of simulation results, the distributed fiber optic sensor is less affected by environmental factors, and its detection distance and anti-interference ability are better than those of high-precision commercial triaxial acceleration sensors.

Phase Unwrapping Method of Φ-OTDR System Based on Recursive-Branch-Cut Algorithm

Phase unwrapping is a crucial technique in phase-sensitive optical time domain reflectometry (Φ-OTDR) systems. Due to the effects of system under-sampling, I/Q imbalance and environmental noise, the traditional method of unwrapping the phase is prone to wrapping or even distortion. In this work, a two-dimensional phase unwrapping method based on the recursive-branch-cut (RBC) algorithm is proposed and studied to improve the accuracy of the demodulated phase waveform. The data near the vibration location is expanded into a two-dimensional wrapped phase map along the time direction. According to the abnormal phase distribution law, the two-dimensional wrapped phase map is divided into sliding windows of different lengths. Under the constraint of ensuring the global continuity of the phase, the local phase is optimized by selecting an appropriate integration path, and the error is minimized, thereby suppressing the propagation of abnormal noise in the global. The experimental results show that in the range of 1-80 Hz, the method can stably increase the upper limit of the system dynamic range by 3.21 dB. At the same time, the system has a good linear strain response capability, and the strain sensitivity is 22.46rad/με×m and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.9997. In addition, the method greatly improves the demodulation characteristics without increasing the generality and practicability of the system, which is beneficial to the fully digital realization of the heterodyne detection technology.

Pilot-scale testing of natural gas pipeline monitoring based on phase-OTDR and enhanced scatter optical fiber cable

In this paper, we present the results of lab and pilot-scale testing of a continuously enhanced backscattering, or Rayleigh enhanced fiber cable that can improve distributed acoustic sensing performance. In addition, the Rayleigh-enhanced fiber is embedded within a tight buffered cable configuration to withstand and be compatible for field applications. The sensing fiber cable exhibits a Rayleigh enhancement of 13 dB compared to standard silica single-mode fiber while maintaining low attenuation of ≤ 0.4 dB/km. We built a phase-sensitive optical time domain reflectometry system to interrogate the enhanced backscattering fiber cable both in lab and pilot-scale tests. In the laboratory experiment, we analyzed the vibration performance of the enhanced backscattering fiber cable and compared it with the standard single-mode telecom fiber. Afterward, we field validated for natural gas pipeline vibration monitoring using a 4-inch diameter steel pipeline operating at a fixed pressure level of 1000 psi, and a flow rate of 5, 10, 15, and 20 ft/s. The feasibility of gas pipeline monitoring with the proposed enhanced backscattering fiber cable shows a substantial increase in vibration sensing performance. The pilot-scale testing results demonstrated in this paper enable pipeline operators to perform accurate flow monitoring, leak detection, third-party intrusion detection, and continuous pipeline ground movement.

Noise analysis in direct detection and coherent detection phase-sensitive optical time-domain reflectometry systems.

This study compares noise and signal-to-noise ratio (SNR) in direct detection and coherent detection fiber-based distributed acoustic sensing (DAS) systems. Both detection schemes employ the dynamic analysis of Rayleigh-backscattered light in phase-sensitive optical time-domain reflectometry (ΦOTDR) systems. Through theoretical and experimental analysis, it is determined that for photodetection filters with a sufficiently narrow bandwidth, the SNR performance of both detection schemes is comparable. However, for filters with poor selectivity, coherent detection was found to exhibit superior performance. These findings provide crucial guidelines for the design of high-performance time-domain DAS systems.

Denoising algorithm of [formula omitted] -OTDR signal based on curvelet transform with adaptive threshold

In this paper, a denoising scheme based on curvelet transform is proposed to improve the signal-to-noise ratio (SNR) for vibration sensing in phase-sensitive optical time-domain reflectometry (Φ-OTDR) systems. The noise level can be estimated based on non-vibration images, which can be used to set the threshold for curvelet coefficients. We further optimize the threshold according to the amplitude distribution of the curvelet coefficients. In the experimental demonstration, when the optical pulse width is 100 ns, the SNR of location information of the 100 Hz vibration events can be increased by 6.93 dB, which proves the effectiveness of the proposed method.

Quadrature Phase-Shift Keying Modulation With Random Coding Pulse for Long-Range ϕ-OTDR

Transient effect in erbium-doped fiber amplifier (EDFA) restricts the application of the phase-sensitive optical time-domain reflectometry (ϕ-OTDR) system with coding probe pulse (CPP) in long-distance vibration sensing. In this paper, a CPP modulation scheme based on phase coding is proposed to suppress the amplitude distortion after optical amplification. Quadrature phase-shift keying (QPSK), which has a high spectrum utilization efficiency, is designed on a I/Q modulator. The random sequence CPP is used in this system, because of the single coding sequence, the signal-to-noise ratio (SNR) can be improved without loss of the sensing bandwidth. Experimental results prove the effectiveness of the scheme. Compared to the conventional modulation scheme at a 50 mW pump power, the distortion coefficient of the CPP after amplification decreases from 57.67% to 9.23%, and the signal-to-interference ratio (SIR) increases from 13.89 dB to16.67 dB. Without distributed amplification, the proposed system successfully located the vibration at 85.652 km under the carrier period of 40 ns, and the SNR of the demodulated signal is up to 21.99 dB.

SNR enhancement with a non-local means image-denoising method for a Φ-OTDR system.

Orthogonal pulse pairs generated by the polarization beam splitter (PBS) and the polarization maintaining-optical switch (PM-PSW) can effectively suppress the polarization fading in phase-sensitive optical time-domain reflectometry (Φ-OTDR) systems, but the PM-PSW also brings a lot of noise when switching the optical path periodically. Therefore, a non-local means (NLM) image-processing method is proposed to enhance the signal-to-noise ratio (SNR) of a Φ-OTDR system. Compared with the existing traditional noise reduction methods based on the one-dimensional signal, the method makes full use of redundant texture and self-similarity of multidimensional data. The NLM algorithm can obtain the estimated denoising result value of current pixels by the weighted average of pixels with similar neighborhood structures in the Rayleigh temporal-spatial image. To validate the effectiveness of the proposed approach, we have carried out experiments on the actual signals obtained from the Φ-OTDR system. In the experiment, a sinusoidal waveform of 100Hz is applied at 20.04km of the optical fiber as a simulated vibration signal. The switching frequency of PM-PSW is set to 30Hz. The experimental result shows that the SNR of vibration positioning curve is 17.72dB before denoising. After using the NLM method based on image-processing technology, the SNR reaches 23.39dB. Experimental results demonstrate that this method is feasible and effective in improving SNR. This will help to realize accurate vibration location and recovery in practical applications.

Denoising of BOTDR Dynamic Strain Measurement Using Convolutional Neural Networks.

The Brillouin optical time domain reflectometry (BOTDR) system measures the distributed strain and temperature information along the optic fibre by detecting the Brillouin gain spectra (BGS) and finding the Brillouin frequency shift profiles. By introducing small gain stimulated Brillouin scattering (SBS), dynamic measurement using BOTDR can be realized, but the performance is limited due to the noise of the detected information. An image denoising method using the convolutional neural network (CNN) is applied to the derived Brillouin gain spectrum images to enhance the performance of the Brillouin frequency shift detection and the strain vibration measurement of the BOTDR system. By reducing the noise of the BGS images along the length of the fibre under test with different network depths and epoch numbers, smaller frequency uncertainties are obtained, and the sine-fitting R-squared values of the detected strain vibration profiles are also higher. The Brillouin frequency uncertainty is improved by 24% and the sine-fitting R-squared value of the obtained strain vibration profile is enhanced to 0.739, with eight layers of total depth and 200 epochs.

SNR improvement for Φ-OTDR with sparse representation denoising method

A Rayleigh backscattering (RBS) signal denoising method based on sparse representation is proposed in this paper. The method can effectively reduce the noise of the RBS signal and improve the signal-to-noise ratio (SNR) of vibration location results for the phase-sensitive optical time-domain reflectometry (Φ-OTDR) system. In the experiments, the initial dictionary is constructed by discrete cosine transform, the dictionary matrix and sparse matrix are updated by the k-singular value decomposition method, and then RBS signal reconstruction is executed. In this process of reconstruction, the effective signals can be sparsely represented by the linear combination of dictionary atoms, while the noise signals are discarded as the residual between the original RBS signals and the reconstructed target signals, which makes the noise separated and improves the SNR of the system. To validate the feasibility and effectiveness of the proposed approach for the SNR enhancement, we have carried out experiments on the actual signals obtained from the Φ-OTDR system. The experimental result indicates that the average increment of SNR is 3.44 dB at 10 km, 20 km, 30 km, and 40 km of the optical fiber after using the sparse representation denoising method. Moreover, the vibration detection range is enhanced to 80 km and the SNR reaches 3.27 dB. This method provides a novel scheme to improve the SNR for the Φ-OTDR system.

Accurate Detection and Localization of Water Pipe Leaks through Model-Based TDR Inversion

The problem of water scarcity affects many areas of the world due to water mismanagement and overconsumption and, more recently, to climate change. Monitoring the integrity of distribution systems is, therefore, increasingly important to avoid the waste of clean water. This paper presents a new signal processing technique for enhancing the performance of the methodology of leak detection in water distribution pipes based on time domain reflectometry (TDR). The new technique is based on a particular kind of TDR inversion (spatial TDR) based on a "gray-box" lumped parameter model of the system. The model does not include, e.g., radiative phenomena, non-TEM (transverse electromagnetic) modes etc. but is capable of reproducing accurately the complicated reflectograms obtained by a TDR leak detection system assuming a proper profile of capacitance per unit length along the sensing element. Even more importantly, the model is identified using only the reflectograms taken by the system with very little prior information about the system components. The developed technique is able to estimate with good accuracy, from reflectograms with unclear or ambiguous interpretation, the position and the extension of a region where water is located. The measurement is obtained without prior electromagnetic characterization of the TDR system components and without the need of modeling or quantifying a number of electromagnetic effects typical of on-site measurements.

Dynamic temperature-strain discrimination using a hybrid distributed fiber sensor based on Brillouin and Rayleigh scattering.

We present a distributed fiber sensor capable of discriminating between temperature and strain while performing low-noise, dynamic measurements. This was achieved by leveraging recent advances in Brillouin and Rayleigh based fiber sensors. In particular, we designed a hybrid sensor that combines a slope-assisted Brillouin optical time domain analysis system with a Rayleigh-scattering-based frequency scanning optical time domain reflectometry system. These sub-systems combine state-of-the-art sensitivity with the ability to perform both dynamic and quasi-static measurements. This enabled a hybrid system capable of temperature/strain discrimination with a quasi-static temperature resolution of 16 m°C and a strain resolution of 140 nɛ along 500 m of single mode fiber with 5 m spatial resolution. In contrast to previously reported techniques, this approach also enabled dynamic measurements with a bandwidth of 1.7 kHz and temperature (strain) noise spectral density of 0.54 m°C/√Hz (4.5 nɛ/√Hz) while temperature/strain cross-sensitivity was suppressed by at least 25 dB. This represents a dramatic improvement in measurement speed and sensitivity compared with existing techniques capable of temperature/strain discrimination in standard single mode fiber.

An Ameliorated Denoising Scheme Based on Deep Learning for Φ-OTDR System With 41-km Detection Range

In recent years, denoising methods for improving the performance of the phase-sensitive optical time-domain reflectometry ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula> -OTDR) system have been restricted by the deficiencies of time-consuming and limited denoising effect. In this work, a trained convolutional neural network (CNN)-based image denoising model is proposed to greatly eliminate the unwanted noises in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula> -OTDR-based sensing system. First, the given Rayleigh backscattering traces are acquired and preprocessed through adjacent differentiation and two-dimensionalization. Second, the 2-D preprocessed data are converted into a noisy gray-scale image and sent into the CNN model for training and testing. Third, the CNN model outputs a corresponding denoised gray-scale image, which can be further analyzed by reconverting it into a series of denoised Rayleigh backscattering traces. Finally, a series of experiments are carried out to demonstrate the effectiveness of the proposed denoising scheme. Experimental results show that, in allusion to the vibration signal with different intensities along the 41-km optical sensing fiber, the trained CNN model achieves a signal-to-noise ratio (SNR) enhancement of about 20 dB. Compared with the conventional methods based on wavelet and empirical mode decomposition (EMD), the proposed denoising scheme demonstrates characteristics of robustness, well spatial resolution reservation, and high efficiency. It is believed that the trained CNN model has great potential to be deployed on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula> -OTDR system for real-time denoising.

Denoising method based on VMD-PCC in φ-OTDR system

To improve the signal-to-noise ratio (SNR) of vibration signal in phase-sensitive optical time domain reflectometry (φ-OTDR) system, a vibration signal denoising method based on variational mode decomposition (VMD) and Pearson correlation coefficient (PCC) is proposed. The phase signal demodulated by I/Q demodulation is further processed by a denoising method consisting of K layers VMD, where K was determined by calculating the ratio of adjacent intrinsic mode functions (IMFs) center frequencies, the Pearson correlation coefficient (PCC) estimation between each component and phase signal, and distortion and noise casting by discarding the IMF components with the PCC less than a threshold of 0.4 and the residual component of decomposition. The denoising method was verified in a 2 km φ-OTDR system in the scenarios of single 200 Hz frequency vibration at one position, two single frequencies of 200 Hz and 500 Hz vibration at two positions with minimum separation distance defined by the pulse width, and a multi-frequency vibration of 200 Hz, 1200 Hz and 2500 Hz at one position. And the demodulated phase signal by I/Q demodulation was processed by the proposed method, and further compared with the wavelet decomposition, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD). Experimental results show that the VMD-PCC method is superior to the other methods compared in improving the SNR of vibration signals and has excellent adaptability and effectiveness in φ-OTDR system.