Phase-sensitive Optical Time-domain Reflectometer Research Articles

Harmonic analysis of phase-sensitive optical time-domain reflectometer

This study innovatively proposes a method of using harmonics to mitigate the influence of fading noise in Phase-Sensitive Optical Time-Domain Reflectometer (Φ-OTDR). Numerical modeling was conducted to investigate the behavior of the Φ-OTDR. A section optical fiber is coiled around a PZT (piezoelectric transducer). By applying a signal to the PZT, it emulates an external vibration. The response of the simulated vibration was extracted using phase demodulation algorithm. In the response spectrum of the vibration, the fundamental frequency may be submerged because of the fading noise, causing the useful information to be obscured. However, the region impacted by fading noise in the fundamental frequency is unaffected in certain harmonic components. Thus, information from the harmonics can be used to compensate for the missing information in the fundamental frequency. By analyzing multiple harmonic components, the accuracy of vibration detection and localization can be improved. Experiment was performed to corroborate the findings, and the results demonstrated good consistency with the simulated data. Both the simulation and the experiment proved that considering harmonics can enhance the measurement accuracy of Φ-OTDR. This discovery provides new insights and methods for the application of Φ-OTDR.

Research on fiber OTDR signal recognition and classification based on the ViT model

In the field of event recognition for phase-sensitive optical time-domain reflectometry (-OTDR), convolutional neural networks (CNNs) have been the mainstream tool. However, Transformer models, with their self-attention mechanism, have provided a new perspective for image recognition tasks. This paper proposes a -OTDR event recognition method based on the Transformer model and experimentally demonstrates its significant advantages over CNNs. The method utilizes an attention-based feature extraction approach to recognize distributed fiber optic sensing signals in a phase-sensitive optical time-domain reflectometer (-OTDR). In perimeter defense applications, various interferences and noise severely affect the recognition of -OTDR sensing signals, leading to false alarms, making accurate signal recognition both challenging and necessary. Extracting signal features and using machine learning classification models to recognize signals has been a research hotspot in recent years. The effectiveness of feature extraction is crucial to classification performance. This project introduces a temporal-spatial dual attention mechanism into the feature extraction of fiber optic sensing signals to improve the accuracy and robustness of signal recognition.

Analysis of Field Trial Results for Excavation-Activities Monitoring with φ-OTDR.

Underground telecommunication cables are highly susceptible to damage from excavation activities. Preventing accidental damage to underground telecommunication cables is critical and necessary. In this study, we present field trial results of monitoring excavation activities near underground fiber cables using an intensity-based phase-sensitive optical time-domain reflectometer (φ-OTDR). The reasons for choosing intensity-based φ-OTDR for excavation monitoring are presented and analyzed. The vibration signals generated by four typical individual excavation events, i.e., cutting, hammering, digging, and tamping at five different field trial sites, as well as five different mixed events in the fifth field trial site were investigated. The findings indicate that various types of events can generate vibration signals with different features. Typically, fundamental peak frequencies of cutting, hammering and tamping events ranged from 30 to 40 Hz, 11 to 15 Hz, and 30 to 40 Hz, respectively. Digging events, on the other hand, presented a broadband frequency spectrum without a distinct peak frequency. Moreover, due to differences in environmental conditions, even identical excavation events conducted with the same machine may also generate vibration signals with different characteristics. The diverse field trial results presented offer valuable insights for both research and the practical implementation of excavation monitoring techniques for underground cables.

An improved denoising method for [formula omitted]-OTDR signal based on the combination of temporal local GMCC and ICEEMDAN-WT

A noise reduction method based on the combination of improved complete ensemble empirical mode decomposition with adaptive noise and temporal local generalized maximum cross-correlation entropy and wavelet threshold (ICEEMDAN-TLGMCC-WT) is proposed to improve the noise rejection ratio (NRR) of phase-sensitive optical time-domain reflectometer (φ-OTDR) system signals. Firstly, the principle of the proposed ICEEMDAN-TLGMCC-WT denoising method is introduced. On this basis, experiments are carried out to verify the feasibility of the noise reduction algorithm for non-stationary signals. and the results demonstrate that the vibration event can be detected with an improved average NRR of 72.18 dB and a root mean square error (RMSE) 0.017 within 5 km sensing distance by employing the ICEEMDAN-TLGMCC-WT denoising method, which confirm the effectiveness of proposed scheme for the performance improvement of φ-OTDR system.

A hand-crafted φ-OTDR event recognition method based on space-temporal graph and morphological object detection

In the long-range pipeline monitoring application of Fiber Optic Distributed Vibration Sensor (FODVS), instantaneous vibration event with high energy generates a pair of invert-V signature in the space-temporal graph, providing clear characteristic for event recognition. This paper proposed an event recognition method utilizing FODVS and hand-crafted morphological object detection algorithm. Phase-sensitive Optical Time Domain Reflectometer (φ-OTDR) was employed as the distributed sensor to collect the real field space-temporal graph data containing invert-V signature. Bandpass filter and Gaussian blur were utilized to suppress random noise and enhance the invert-V signature, then a hand-crafted morphological object detection method was proposed to recognize the invert-V pattern. The proposed method was proved to be able to accurately capture the invert-V signature in the space-temporal graph. This paper provided a concept-proof preliminary demonstration on the hand-crafted FODVS event recognition scheme for pursuing model with good interpretability and strong adaptability to varied environments.

Phase unwrapping error identification and suppression method in φ-OTDR systems based on PELT-VMD-ARIMA

In phase-sensitive optical time-domain reflectometer (φ-OTDR) systems, phase unwrapping errors can distort vibration information. To address this issue, a phase unwrapping error identification and suppression method combining pruned exact linear time (PELT) changepoint detection, variational mode decomposition (VMD), and autoregressive integrated moving average (ARIMA) models, termed PELT-VMD-ARIMA, is proposed. Firstly, the principle of the proposed method is introduced, and its effectiveness is verified through a series of numerical simulation experiments. Next, piezoelectric transducers (PZTs) are employed as seismic sources in experiments involving single-frequency and chirp signals. Compared to the mean-shift method, the proposed method reduces the average root mean square error (RMSE) by 70.36% within 2δ range around the changepoints. Finally, the proposed method was validated through an active source seismic application. The results demonstrate the efficacy of the proposed method in identifying and suppressing phase unwrapping errors, thereby enhancing signal quality. This method enhances the vibration recognition capability of φ-OTDR systems, which facilitates precise distributed acoustic sensing applications.

An SNR Enhancement Method for Φ-OTDR Vibration Signals Based on the PCA-VSS-NLMS Algorithm.

To improve the signal-to-noise ratio (SNR) of vibration signals in a phase-sensitive optical time-domain reflectometer (Φ-OTDR) system, a principal component analysis variable step-size normalized least mean square (PCA-VSS-NLMS) denoising method was proposed in this study. First, the mathematical principle of the PCA-VSS-NLMS algorithm was constructed. This algorithm can adjust the input signal to achieve the best filter effect. Second, the effectiveness of the algorithm was verified via simulation, and the simulation results show that compared with the wavelet denoising (WD), Wiener filtering, variational mode decomposition (VMD), and variable step-size normalized least mean square (VSS-NLMS) algorithms, the PCA-VSS-NLMS algorithm can improve the SNR to 30.68 dB when the initial SNR is -1.23 dB. Finally, the PCA-VSS-NLMS algorithm was embedded into the built Φ-OTDR system, an 11.22 km fiber was measured, and PZT was added at 10.19-10.24 km to impose multiple sets of fixed-frequency disturbances. The experimental results show that the SNR of the vibration signal is 8.77 dB at 100 Hz and 0.07 s, and the SNR is improved to 26.17 dB after PCA-VSS-NLMS filtering; thus, the SNR is improved by 17.40 dB. This method can improve the SNR of the system's position information without the need to change the existing hardware conditions, and it provides a new scheme for the detection and recognition of long-distance vibration signals.

Mixed event separation and identification based on a convolutional neural network trained with the domain transfer method for a φ-OTDR sensing system.

In phase-sensitive optical time domain reflectometer (φ-OTDR) based distributed acoustic sensing (DAS), correct identification of event types is challenging in complex environments where multiple events happen simultaneously. In this study, we have proposed a convolutional neural network (CNN) with a separation module and an identification module to simultaneously separate a mixed event into individual single-event components and identify each type of component contained in the mixed event. The domain transfer method is used in the training, fine-tuning, and testing of the proposed CNN, which saves 94% of the workload for massive DAS data collection and signal demodulation. A fine-tuning stage is added to minimize the impact of the dataset shift between the audio data and DAS data, hence enhancing the separation and identification performance. The model has good noise tolerance and achieves nearly 90% identification accuracy even at a relatively low signal-to-noise ratio (SNR). Compared with the conventional method using DAS data for training, domain transfer using a large amount of diverse audio data for training well generalizes the model to the target domain and hence provides more stable performance with only little degradation of identification accuracy.

Instability Compensation of Recording Interferometer in Phase-Sensitive OTDR.

In the paper, a new method of phase measurement error suppression in a phase-sensitive optical time domain reflectometer is proposed and experimentally proved. The main causes of phase measurement errors are identified and considered, such as the influence of the recording interferometer instabilities and laser wavelength instability, which can cause inaccuracies in phase unwrapping. The use of a Mach-Zender interferometer made by 3 × 3 fiber couplers is proposed and tested to provide insensitivity to the recording interferometer and laser source instabilities. It is shown that using all three available photodetectors of the interferometer, instead of just one pair, achieves significantly better accuracy in the phase unwrapping. A novel compensation scheme for accurate phase measurements in a phase-sensitive optical time domain reflectometer is proposed, and a comparison of the measurement signals with or without such compensation is shown and discussed. The proposed method, using three photodetectors, allows for very good compensation of the phase measurement errors arising from common-mode noise from the interferometer and laser source, providing a significant improvement in signal detection. In addition, the method allows the tracking of slow temperature changes in the monitored fiber/object, which is not obtainable when using a simple low-pass filter for phase unwrapping error reduction, as is customary in several systems of this kind.

Interference fading suppression with a multi-subcarrier pulse in a distributed acoustic sensor

A low-complexity multi-subcarrier pulse generation scheme is proposed to suppress the interference fading in a phase-sensitive optical time-domain reflectometer (Φ-OTDR) based distributed acoustic sensor (DAS) with heterodyne coherent detection. The multi-subcarrier pulse is generated in the digital domain based on the proper clipping operation of a sine signal. The localization and recovery of the disturbance signal are realized by the spectrum extraction and rotated vector sum (SERVS) method. The experimental results show that the occurrences of interference fading can be significantly reduced. The intensity fluctuation is reduced from ∼75 dB to ∼25 dB. Multiple disturbance signals are successfully demodulated to verify the effectiveness of the proposed method.

Interference fading suppression with fault-tolerant Kalman filter in phase-sensitive OTDR

A multi-sensor information fusion algorithm based on fault-tolerant Kalman filter is proposed in phase-sensitive optical time-domain reflectometer (Φ-OTDR) system, for achieving fading-free distributed vibration sensing. Firstly, a fault-tolerant dual-core complementary array model is designed. The Rayleigh scattering signal denoising, and vibration existence judgment of localization points are carried out to obtain the differentiated frequency demodulation results of the sensing points of the dual-core fiber array. Then a fault-tolerant control strategy is used to determine the sensor weight coefficients and vibration judgment coefficients during data fusion processing, and array data fusion is carried out based on time series data using Kalman filter to realize error value identification and filling. The advantage of this method is the combination of redundant data in a complementary way to improve the system stability. The frequency response ranges from 10 Hz to 2400 Hz and the localization accuracy is 98.33%. The influence of key parameters on the frequency demodulation performance of fault-tolerant Kalman filter is discussed, and a standard deviation of 14.6 Hz and an average error of 7.6 Hz are obtained. The demodulation frequency data matrix obtained by the classical demodulation method has a demodulation error probability of 89.18%, which proves the widespread existence of demodulation errors in vibration signals. The fusion error of demodulation frequency is reduced to 0.25 Hz, the frequency demodulation accuracy reaches 100%, and the demodulation error caused by interference attenuation can be completely eliminated. This system based on fault-tolerant Kalman filter has the characteristics of simple multiplexing structure, interference fading resistance and stable demodulation performance.

Event recognition method based on feature synthesizing for a zero-shot intelligent distributed optical fiber sensor

Phase-sensitive optical time domain reflectometer (Φ-OTDR) is an emergent distributed optical sensing system with the advantages of high localization accuracy and high sensitivity. It has been widely used for intrusion identification, pipeline monitoring, under-ground tunnel monitoring, etc. Deep learning-based classification methods work well for Φ-OTDR event recognition tasks with sufficient samples. However, the lack of training data samples is sometimes a serious problem for these data-driven algorithms. This paper proposes a novel feature synthesizing approach to solve this problem. A mixed class approach and a reinforcement learning-based guided training method are proposed to realize high-quality feature synthesis. Experiment results in the task of eight event classifications, including one unknown class, show that the proposed method can achieve an average classification accuracy of 42% for the unknown class and obtain its event type, meanwhile achieving a 74% average overall classification accuracy. This is 29% and 7% higher, respectively, than those of the ordinary instance synthesizing method. Moreover, this is the first time that the Φ-OTDR system can recognize a specific event and tell its event type without collecting its data sample in advance.

Markov transition fields and auto-encoder-based preprocessing for event recognition of Φ-OTDR

To improve the model training efficiency and the classification performance of the phase-sensitive optical time-domain reflectometer (Φ-OTDR) in disturbance events recognition, a preprocessing method based on Markov transition fields (MTF) and auto-encoder (AE) is proposed. The phase time series, derived from demodulation of the original scattering signals, are converted into images by using the MTF method. Subsequently, an auto-encoder is introduced to perform a dimensionality reduction characterization of the MTF images, and the outputs of the encoder will be used as features for classification. The experimental results demonstrate that, compared with directly processing time series using 1-D CNN and classifying MTF images using CNN, the features obtained by the proposed method can accelerate the training process and improve the recognition performance of the classification model. The recognition accuracy for the four classes of events on the fence reaches 95.6%, representing a 12% increase.

Interference fading suppression for distributed acoustic sensor using frequency-shifted delay loop

In order to suppress the interference fading of phase-sensitive optical time domain reflectometer (Φ-OTDR), a multi-frequency detection method based on frequency-shifted delay loop (FSDL) is proposed. A probe pulse train with up to five consistent frequency components are generated by periodic frequency-shifted, amplification, delay and filtering processing in FSDL. The piecewise aggregate approximation (PAA) is introduced to compress the amount of operational data. Wavelet energy spectrum analysis and support vector machine (SVM) are used to extract features and realize the fading classification. With the help of fading label making and restoration, the multi-frequency signals are intelligently aggregated using rotated-vector-sum (RVS). Experimental results show that PAA has applicability in data compression of beat signals. After 5-layer wavelet energy spectrum analysis, the fading binary classification accuracy can reach 96.77 %, and the fading signals identified after segmentation can be restored to the original data. The fading probability based on SVM output labels can be reduced to 1.89 %. The SVM-based classification output labels aggregation shows that the vibration positioning signal-to-noise ratio (SNR) can be increased to 13.52 dB, the phase demodulation curve is smoother, and the demodulation SNR can be up to 17.63 dB. Finally, the R-Square of 0.997 for the amplitude of demodulated vibration signal in repeatability experiment verifies the validity of this effective scheme for fading recognition and suppression.

Fading suppression in the Ф-OTDR system based on a phase-modulated optical frequency comb.

In this paper, what we believe to be a novel method is proposed to suppress the fading effect of the phase-sensitive optical time domain reflectometer (Ф-OTDR) by using a phase-modulated optical frequency comb. In the Ф-OTDR system, intensity distributions of Rayleigh backscattering (RBS) light are different for pulsed probe lights with different central frequencies, therefore the locations of the fading points corresponding to signals of different frequencies are differently distributed, allowing the use of frequency division multiplexing to suppress the fading effects. In the experimental system of this paper, a continuous light in the form of a frequency comb is firstly generated through phase modulation. It is then modulated into a pulsed probe light and injected into the sensing fiber to produce different RBS intensity distributions. Finally, the extracted phase is processed by using the amplitude evaluation method, so that the distorted phase can be eliminated. Fading suppression is achieved using our system, and the effect of suppression is evaluated. By using an equal-amplitude optical frequency comb containing seven frequency components, the fading probability density of the system is dramatically reduced from the range of 5.49%-9.83% to 0.08%. Compared with the conventional system using a single acoustic-optic modulator to generate the frequency shift, the method proposed in this paper features a larger modulation bandwidth and more flexible frequency combination scheme to better suppress the fading effect. This method does not sacrifice the response bandwidth of the system, and the phase delay can be precisely controlled, which helps to fully suppress the fading effect.

Characterization of Laser Frequency Stability by Using Phase-Sensitive Optical Time-Domain Reflectometry

A new method to measure laser phase noise and frequency stability based on the phase-sensitive optical time-domain reflectometry is proposed. In this method, the laser under test is utilized in a phase-sensitive optical time-domain reflectometer, which employs phase-modulated dual pulses and acts as an optical frequency discriminator: laser frequency fluctuations are deduced from the analysis of the reflectometer data corresponded to phase fluctuations along the vibration-damped and thermally insulated fiber spool. The measurement results were validated by comparison with direct optical heterodyning of the tested and more coherent reference lasers. The use of dual pulses generated by an acousto-optic modulator makes it easy to adjust the time delay during measurements, which distinguishes favorably the proposed method from standard optical frequency discriminators. The method is suitable for testing highly coherent lasers and qualifying their parameters such as linear drift rate, random frequency walk rate, white frequency noise (which is directly related to laser instantaneous linewidth), and flicker noise level.

Multi-Event Location Denoising Scheme for φ-OTDR Based on FFDNet Network

In order to improve the signal-to-noise ratio (SNR) of vibration sensing in the phase-sensitive optical time-domain reflectometer (φ-OTDR) system, a fiber sensing signal processing method based on the FFDNet convolutional neural network is proposed in this paper. In the network, the concept of residual learning is introduced, which involves constructing a residual mapping and utilizing multi-layer convolutional neural networks to learn the noise distribution present in the original image. The denoised result can be obtained by subtracting the learned noise from the original image. We have built a φ-OTDR system based on coherent detection, using three PZTs as simulated vibration sources and a series of experiments at 200 Hz, with each experiment simulating a single vibration event or multiple vibration events by setting different intensities. The experimental results demonstrate that the FFDNet based fiber optic sensing signal processing method enhances the SNR to 37.84 dB, 37.11 dB, and 37.31 dB, respectively, while preserving vibration signal details more effectively than wavelet denoising and Gaussian filtering techniques. The trained FFDNet model has great potential for improving the performance of the φ-OTDR system and has some practical application value.

Modulation instability in long distance φ-OTDR system

Recent trends in long distance phase-sensitive optical time-domain reflectometer (φ-OTDR) have led to a proliferation of studies on modulation instability (MI). However, most of these studies overlook the inaccuracies of MI gain spectrum based on the linear stability analysis of the nonlinear Schrödinger equation in the case of long distance over about 10 km. Here, we conducted a comprehensive exploration of MI characteristics in the φ-OTDR system, including the effects of input power, detection distance, and background noise on the MI evolution process. Experiments confirmed the presence of an intrinsic spectral component of MI surrounding the signal in long-distance detection, which has not been reported previously. Further, we proposed a fitting expression using numerical simulation to determine the optimal input power for the φ-OTDR system. Compared to traditional threshold formulas, it does not require complex theoretical derivations and demonstrated significantly higher accuracy, particularly in systems with ultra-long detection distances. The deviation was less than 10%, demonstrating its potential as a more precise and simplified method of determining optimal input power. This could help improve the efficiency and reliability of these systems, leading to further advancements in the field of MI research.

A Distributed Acoustic Sensor with a 120-km Sensing Range Based on a Phase-Sensitive Optical Time-Domain Reflectometer and a Remotely Pumped Erbium-Doped Fiber Amplifier

A Distributed Acoustic Sensor with a 120-km Sensing Range Based on a Phase-Sensitive Optical Time-Domain Reflectometer and a Remotely Pumped Erbium-Doped Fiber Amplifier

Research on Pattern Recognition Method for φ-OTDR System Based on Dendrite Net

The phase-sensitive optical time-domain reflectometer (φ-OTDR) is commonly used in various industries such as oil and gas pipelines, power communication networks, safety maintenance, and perimeter security. However, one challenge faced by the φ-OTDR system is low pattern recognition accuracy. To overcome this issue, a Dendrite Net (DD)-based pattern recognition method is proposed to differentiate the vibration signals detected by the φ-OTDR system, and normalize the differential signals with the original signals for feature extraction. These features serve as input for the pattern recognition task. To optimize the DD for the pattern recognition of the feature vectors, the Variable Three-Term Conjugate Gradient (VTTCG) is employed. The experimental results demonstrate the effectiveness of the proposed method. The classification accuracy achieved using this method is 98.6%, which represents a significant improvement compared to other techniques. Specifically, the proposed method outperforms the DD, Support Vector Machine (SVM), and Extreme Learning Machine (ELM) by 7.5%, 8.6%, and 1.5% respectively. The findings of this research paper indicate that the pattern recognition method based on DD and optimized using the VTTCG can greatly enhance the accuracy of the φ-OTDR system. This improvement has important implications for various applications in industries such as pipeline monitoring, power communication networks, safety maintenance, and perimeter security.