Φ-OTDR System Research Articles

Intensity and phase stacked analysis of a Φ-OTDR system using deep transfer learning and recurrent neural networks.

Distributed acoustic sensors (DAS) are effective apparatuses that are widely used in many application areas for recording signals of various events with very high spatial resolution along optical fibers. To properly detect and recognize the recorded events, advanced signal processing algorithms with high computational demands are crucial. Convolutional neural networks (CNNs) are highly capable tools to extract spatial information and are suitable for event recognition applications in DAS. Long short-term memory (LSTM) is an effective instrument to process sequential data. In this study, a two-stage feature extraction methodology that combines the capabilities of these neural network architectures with transfer learning is proposed to classify vibrations applied to an optical fiber by a piezoelectric transducer. First, the differential amplitude and phase information is extracted from the phase-sensitive optical time domain reflectometer (Φ-OTDR) recordings and stored in a spatiotemporal data matrix. Then, a state-of-the-art pre-trained CNN without dense layers is used as a feature extractor in the first stage. In the second stage, LSTMs are used to further analyze the features extracted by the CNN. Finally, a dense layer is used to classify the extracted features. To observe the effect of different CNN architectures, the proposed model is tested with five state-of-the-art pre-trained models (VGG-16, ResNet-50, DenseNet-121, MobileNet, and Inception-v3). The results show that using the VGG-16 architecture in the proposed framework manages to obtain a 100% classification accuracy in 50 trainings and got the best results on the Φ-OTDR dataset. The results of this study indicate that pre-trained CNNs combined with LSTM are very suitable to analyze differential amplitude and phase information represented in a spatiotemporal data matrix, which is promising for event recognition operations in DAS applications.

Distributed Impact Wave Detection in Steel I-Beam with a Weak Fiber Bragg Gratings Array.

In this paper, acoustic, dynamic and static strain variations along a steel I-beam generated by an impact load are reconstructed simultaneously within a single measurement. Based on the chirped pulse φ-OTDR system with the single-shot measurement technique, both a higher strain-sensing resolution and a higher measurable vibration frequency are achieved. In addition, a weak fiber Bragg gratings array (WFBGA) with enhanced Rayleigh reflection is employed as a sensor, providing high signal-to-noise ratio Rayleigh traces, resulting in lower measurement uncertainty. In the experiments, the damping constant and fundamental frequency of the damped harmonic oscillator could then be measured based on the recovered strain variation profile for further structural health analysis. Compared with commercial strain gauges, linear potentiometers, and OFDR systems, the proposed sensing system ensures a distributed, quantitative, and high-frequency sensing ability, with an extensive range of potential applications.

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.

Influences of pulse parameters on the performance of double-pulse [formula omitted]-OTDR

In the past decade, an increasing number of studies of double-pulse ϕ-OTDR systems have been reported, but so far, there are no studies specifically analyzing the influences of double pulses on system performance. In this paper, we construct a theoretical model of the double-pulse ϕ-OTDR system, analyze the interference process of the system theoretically, and make conjectures on spatial resolution and demodulation range. On the basis, we build a double-pulse ϕ-OTDR experimental setup based on Sagnac balanced interferometer. The feasibility of the proposed theory is verified by experiments altering the probe pulse parameters and localizing two-point disturbances at different frequencies. The results also indicate when pulse width is greater than the pulse interval, this system offers more accurate positioning capabilities and a continuous demodulation range with high fidelity. The conclusions provide a reference for the selection of appropriate double-pulse parameters for similar systems, and the theoretical model offers a methodological suggestion for exploring other types of double pulses or even multi-pulse systems.

Quantitative detection of multi-frequency disturbance signal by ϕ-OTDR system

Recently, the combination of pattern recognition technology and distributed fiber sensing systems has become increasingly common, so whether the disturbance signal can be well recovered has become increasingly important. To verify the recovery and linear response of a distributed fiber optic sensing system to multi-frequency disturbance signals, a heterodyne coherent detection system for phase-sensitive optical time-domain reflectometry is developed. The output beat signal is extracted using the digital in-phase/quadrature demodulation algorithm. The signal can be precisely located on a 7 km length range, and the disturbance signal can be restored well through the phase information. Not only the superposition signal composed of the same signal but also that composed of different kinds of signals can be successfully restored. A fast Fourier transform algorithm is used to obtain the frequency information of the superimposed signal. Combined with the use of a finite impulse response filter, the superposed signal is decomposed according to its frequency components, which perfectly restores the two signals before they are superimposed. In addition, their amplitude is highly linear with the driving voltage of the piezoelectric transducer. The system can fully retain the details of each frequency component in the recovery of multi-frequency disturbance signals. More importantly, in field experiments, the disturbance behavior is well recovered, which has broad prospects in the application of perimeter security.

Noise Reduction Based on Adaptive Prediction Fitting Algorithm for a Heterodyne Φ-OTDR System

Aiming to reduce the noise better in distributed optical fiber vibration sensing system, an adaptive predictive filtering algorithm is proposed to improve the overall signal-to-noise ratio (SNR) of vibration detection. A phase-sensitive optical time domain reflectometric ( <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 based on heterodyne detection with dual AOMs is designed to reduce the requirement of DAQ high sampling rate, and an adaptive prediction noise reduction method based on normalized least mean square (NLMS) is adopted to reduce the non-stationary noise. Experimental results show that the SNR is increased from 8.14dB to 22.31dB. Compared with moving difference average method, wavelet denoising method and Prewitt edge detection method, the SNR of the adaptive prediction noise reduction algorithm is the highest, which would have potential application in disturbance detection under a strong noise environment.

Suppression of Modulation Instability Induced Phase Noise in the Long-Haul Phase-Sensitive Optical Time Domain Reflectometry.

Modulation instability (MI) is the main limitation factor of the maximum optical power in long-haul phase-sensitive optical time domain reflectometry (Φ-OTDR), and induces signal fading and serious phase noise. In this paper, a method of coherent seed injection is proposed to suppress the MI-induced phase noise in long-haul Φ-OTDR. The spontaneous MI is suppressed by stimulating induced MI in an optical fiber. The visibility of the signal in Φ-OTDR is enhanced and the phase noise is suppressed significantly. This paper offers an effective method to increase the maximum input power with the MI-induced phase noise suppressed in the long-haul Φ-OTDR system. As a result, the maximum input power and sensing distance can be potentially increased, which is greatly beneficial to the enhancement of the performance of long-haul Φ-OTDR.

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.

A Method on Vibration Positioning of Φ-OTDR System Based on Compressed Sensing

To improve the accuracy of vibration location information of the Φ-OTDR system, a method of optical fiber sensing signal processing based on Compressed Sensing (CS) is proposed in this paper. First, a sensing matrix is constructed by sparse array and observation array, and the mathematical modeling of CS is established. Then the reconstruction algorithm is designed to complete the accurate reconstruction of the vibration signal with a small number of sampling values. Second, the time domain signal composed of all scattered light at the vibration source is analyzed to complete the measurement of vibration frequency. Finally, a heterodyne Phi-OTDR system is built, using PZT to simulate the vibration source. And 36 scattered light information is continuously collected at the frequency of 100 Hz and 500 Hz. The experimental verification of the CS method in this paper is carried out, besides, the signal-to-noise ratio (SNR) is compared with the traditional difference method, Empirical Mode Decomposition (EMD) method, and Variational Mode Decomposition (VMD) method. The experimental results show that the SNR of this method is improved to 40.41 dB and 30.62 dB, the optimal spatial resolution is 9m, and the maximum relative error of frequency measurement is 8.1%. Compared with the results of traditional I/Q demodulation, the frequency information obtained by the two methods is consistent, and there is no obvious deviation from the actual frequency of PZT. While improving the SNR of the system, this method can greatly simplify the signal processing process and improve the positioning accuracy of the system, which has a certain practical application value in the fields of infrastructure monitoring, resource exploration, and vibration detection.

Event recognition method based on dual-augmentation for a Φ-OTDR system with a few training samples.

Thanks to the development of machine learning and deep learning, data-driven pattern recognition based on neural network is a trend for Φ-OTDR system intrusion event recognition. The data-driven pattern recognition needs a large number of samples for training. However, in some scenarios, intrusion signals are difficult to collect, resulting in the lack of training samples. At the same time, labeling a large number of samples is also a very time-consuming work. This paper presents a few-shot learning classification method based on time series transfer and cycle generative adversarial network (CycleGAN) data augmentation for Φ-OTDR system. By expanding the rare samples based on time series transfer and CycleGAN, the number of samples in the dataset can finally meet the requirement of network training. The experimental result shows that even when the training set has two minor classes with only two samples, the average accuracy of the validation set with 5 classification tasks can still reach 90.84%, and the classification accuracy of minor classes can reach 79.28% with the proposed method.

On-line status monitoring and surrounding environment perception of an underwater cable based on the phase-locked Φ-OTDR sensing system.

A newly designed phase-locked (PL) Φ-OTDR system was proposed and instrumented. Field tests of water impact, anchor damage towing and tide diagnosing were carried out in a natural freshwater lake as well as the East China Sea. Personnel movement trajectory monitoring and ship flow monitoring were carried out by a buried cable along the floodplain of the Yangtze River. It proved that the proposed system can monitor the real-time status and sense the surrounding environment of existing underwater communication cables, which could be helpful for the maintenance of the cable itself as well as underwater information collection.

Detection Range Enhancement for Φ-OTDR Using Semantic Image Segmentation

Aiming to reduce the complexity of Φ-OTDR system introduced by traditional distributed optical amplification technology in the pursuit of long-distance sensing, a novel detection range enhancement method with semantic image segmentation based on deep learning is presented. In this study, the Rayleigh backscattered curves for detecting vibration signals are used to construct the temporal-spatial image. The encoder-decoder structure and atlas spatial pyramid pool model are adopted to capture the boundary of vibration area and multi-scale context information from the temporal-spatial image. The semantic extraction of vibration position is then realized by binary classification of image pixels. In experiments, this method significantly improves the signal-to-noise ratio of the vibration location results. The signal-to-noise ratio can reach 37.84 dB, 34.28 dB, 34.09 dB, and 32.17 dB at 4 km, 10 km, 20 km, and 40 km, respectively. With this approach, the vibration detection range is enhanced to about 80 km without adding any traditional distributed optical amplification structure. Furthermore, within this enhanced detection range, the vibration signals with different frequencies and different intensities can be located and multi-point vibration can also be verified experimentally. This method provides a novel means for the detection range enhancement of Φ-OTDR system.

Research on RP-EDF amplification characteristics based on φ-OTDR system

Extending the sensing distance is of great significance for expanding the application of phase-sensitive optical time-domain reflectometry (φ-OTDR) in remote perimeter monitoring. Due to the inevitable fiber loss, the optical signal intensity decays exponentially with the length of the sensing fiber, and the sensing distance is greatly limited. In this study, a remote-pumped single-ended incident optical signal amplification scheme using erbium-doped fiber (EDF) is reported, and the factors affecting the amplification effect are comprehensively investigated. The experimental results show that the optimal amplification effect can be achieved when the input signal optical power is − 20 dB, the input signal optical wavelength is 1550 nm, the pump power is 100 mW and the erbium-doped fiber length is 10 m.

Interference Fading Suppression Using Active Frequency Transformation Method With Auxiliary Interferometer Feedback

In order to eliminate the distortion of demodulated signal caused by the incomplete suppression of interference fading, an active frequency transformation (AFT) method is proposed for phase-sensitive optical time domain reflectometry (Φ-OTDR). An auxiliary interferometer feedback structure combined with Φ-OTDR is firstly used for the identification of signal distortion caused by the fading state at demodulation positions. The correlation coefficient of vibration signals detected by auxiliary interferometer and Φ-OTDR system can be considered as the feedback variable. The feedback process is then realized by the modulation frequency transformation of phase modulator. Experimental results show that the fading probability can be reduced to 1.58% by means of multi-frequency detection. With the AFT from 25 MHz to 30 MHz, the distortion of demodulated signal can be eliminated with an improved correlation coefficient up to 0.9898.

Enhanced Signal-Associated Noise in a φ-OTDR System

Owing to their high sensitivity with respect to external measurands, Rayleigh-based distributed optical fiber sensors (DOFS) find their way into applications in many industrial and academic sectors. To further transcend the limit of these sensors in terms of sensing range, low spatial resolution, speed and accuracy of the measurement, improving the signal-to-noise ratio (SNR) of the system plays a pivotal role. Out of the several existing techniques for enhancing the SNR, one such method, solely dedicated to the Rayleigh-based sensors, is through intrinsically increasing the backreflected signal of the fibers. The enhanced backreflected signal provided by such fibers, generally known as reflection-enhanced fibers (REF), is rigorously analysed in the present work. It is inferred from the analysis that the enhanced signal is essentially accompanied by enhanced signal-dependent noises, which can adversely affect their performance. For instance, when a performance comparison is carried out between the REF and a standard single-mode fiber (SMF) under identical experimental conditions, due to their different intrinsic backreflected signal levels, the two fibers experience dissimilar noise regimes leading to an erroneous estimation of the performance of the former. This necessitates the optimisation of the interrogation system and the experimental parameters while employing such fibers for specific sensing applications. Additionally, a distributed temperature measurement is presented by taking advantage of the enhanced SNR of a 100 m long REF exhibiting a sub-mK temperature uncertainty of 0.5 mK at metric spatial resolution yielding a sixfold improvement compared to the 3 mK uncertainty of an SMF.

Signal Recognition of φ-OTDR System Based on Wavelet Packet Decomposition and SVM

Signal Recognition of φ-OTDR System Based on Wavelet Packet Decomposition and SVM

Interference-Fading-Suppressed Pulse-Coding Φ-OTDR Using Spectrum Extraction and Rotated-Vector-Sum Method

With phase-sensitive optical time-domain reflectometry (Φ-OTDR), an optic fiber can be used as a distributed vibration sensor to perceive the external disturbance along the fiber through the phase variation of the Rayleigh backscatter light. However, due to the inevitable interference fading phenomenon, the signal-to-noise ratio (SNR) of Rayleigh backscatter light at certain points is insufficient, thus affecting the fidelity of vibration sensing. In this paper, we propose the spectrum extraction and the rotated-vector-sum (SERVS) method to suppress the interference fading in the pulse-intensity-coding Φ-OTDR, which applies Golay complementary sequence through acousto-optic modulator (AOM). Benefited from the easier implementation, the experimental results demonstrate that the percentage of fading points can be reduced from 2.98% to 0.33% significantly and the standard deviation of differential phase is also decreased from 0.6556 rad to 0.2901 rad. Also the minimum level of averaged power spectral density (PSD) of the differential phase at whole locations along the fiber is reduced about 6 dB/Hz effectively. For the demodulated disturbance signal, no influence on the SNR is observed thus it maintains a SNR improvement of 8.6 dB compared to the traditional single pulse Φ-OTDR system, achieving a noise floor of 2.38 pϵ/√Hz.

Time attention analysis method for vibration pattern recognition of distributed optic fiber sensor

The distributed optical fiber sensing technology based on phase-sensitive optical time-domain reflectometer (Φ-OTDR) has the advantage of multi-point vibration monitoring simultaneously. To improve the robustness of the recognition system, a deep learning model is designed to extract time-frequency sequence correlation from signals and spectrograms. This lightweight plug-and-play convolutional neural network architecture achieves better performance with a lower number of extra parameters and computations. This study is verified on a vibration dataset containing eight different scenarios collected by a Φ-OTDR system, and the classification accuracy of 96.02% is achieved.

Orthogonal Imbalance Compensation Method of Φ-OTDR System Based on RLS Algorithm

Aiming at the quadrature unbalance phenomenon in the phase-sensitive optical time domain reflectometry ( $\Phi $ -OTDR) system, a method of quadrature unbalance compensation for the $\Phi $ -OTDR system based on the recursive least square (RLS) algorithm is studied and demonstrated to improve the accuracy of the demodulated phase waveform. Firstly, the DC residual term of the quadrature imbalance is eliminated by calculating the mean value of the envelope. Further, the cyclic symmetry of the AC orthogonal unbalance is tracked to complete parameter estimation, and then the orthogonal unbalance correction is completed through an adaptive filtering method. The experimental results show that under the condition of the detection light pulse with a repetition frequency of 5 KHz and a pulse width of 100 ns, the RLS compensation algorithm greatly improves the demodulation characteristics of the system and suppresses the quadrature imbalance. It is of great significance to the practical process of the $\Phi $ -OTDR system.