Phase-sensitive Optical Time-domain Reflectometry Research Articles

Suppression of the Interference Fading in Heterodyne Φ‐OTDR with Fast Signal Synthesis Algorithm

Abstract With the increasing diversity of application scenarios, there is a growing demand for distributed acoustic sensing (DAS) systems to monitor the environment without false alarms.
In DAS system based on phase-sensitive optical time-domain reflectometry (Φ-OTDR), interference fading significantly lead to a large number of false alarms in the system, which affecting the system's monitoring capability. Before the phase demodulation process, Φ-OTDR system generally requires additional time for interference fading suppression.
Therefore, the rapid acquisition of a fading-free signal before phase demodulation benefits the use of Φ-OTDR in real-time demanding scenarios. In this paper, based on the time-domain stitching and complex-field moving average, a fast signal synthesis (FSS) algorithm is proposed, enabling the quick synthesis of signals while maintaining fading suppression performance. Experimental results show that the signal intensity obtained by FSS algorithm fluctuates within 27 dB, while the intensity of the conventional denoising algorithm fluctuates more than 50 dB. Meanwhile, this method is more rapid than the rotated-vector-sum (RVS) algorithm, and the time consumption of signal synthesis is only 26.58% of that of the RVS algorithm. FSS algorithm provides a new and faster way to synthesize signals for interference fading suppression. Benefiting from this, FSS algorithm further improves the real-time performance of the Φ-OTDR system.

Performance enhancement of optical pulse coding phase-sensitive OTDR system with genetic-optimized code

Optical pulse coding (OPC) in phase-sensitive optical time-domain reflectometry (φ-OTDR) traditionally relies on standard coding sequences as probe pulse trains, which usually suffer the problem of distorted probe pulse caused by the gain saturation in erbium-doped fiber amplifier (EDFA). To improve the signal-to-noise ratio (SNR) and mitigate distortion caused by the EDFA gain saturation, a genetic-optimized code (Go-code) based OPC φ-OTDR (GOPC φ-OTDR) scheme is proposed in this paper. The unique Go-code sequence generated by the distributed genetic algorithm (DGA) is explored in the field of φ-OTDR for the first time, and the coding sequence was optimized to adapt to the vibration sensing requirements in φ-OTDR. Theoretical analyses focused on EDFA gain saturation effects and coding gain were conducted. The experiment of GOPC φ-OTDR demonstrated significant improvements compared with Golay φ-OTDR, achieving less distortion in decoding response and approximately 7.48 dB phase demodulation SNR enhancement within a 10-km sensing range. This research marks Go-code's pioneering exploration into φ-OTDR, demonstrating substantial advancements in optimization metrics, and opening avenues for exploring advanced OPC technologies.

Novel mining conveyor monitoring system based on quasi-distributed optical fiber accelerometer array and self-supervised learning

Belt conveyors in mining are crucial, with downtime leading to significant losses and safety hazards. Unplanned shutdowns often result from idler failures. To address this, an online monitoring system for continuous idler health assessment is proposed. Considering the large number and dense spatial distribution of idlers over long distances, this work presents a system that utilizes a quasi-distributed optical fiber accelerometer array. This array incorporates phase-sensitive optical time domain reflectometry (Phase-OTDR) interrogation technology and ultra-weak fiber Bragg gratings (UWFBGs) to effectively capture idler vibrations. The designed array achieves high-sensitivity vibration sensing with a sensitivity of 2.4 rad/g and a resolution of 1.7 mg/Hz. After collecting the vibrations of idlers by the designed accelerometer array, an automatic fault classification algorithm based on self-supervised learning (SSL) is introduced, which requires only a small number of labeled samples. By leveraging large amount of unlabeled data in the pretext task, the algorithm efficiently extracts latent features from the quasi-distributed accelerometer array. A diagnosis accuracy of 95.37 % can be achieved on a seven-class classification task with only 3.6 % labeled data (16 samples/class). This system offers a promising solution for idler monitoring, combining high sensitivity, distributed measurement capabilities, enhanced security, and superior fault detection accuracy.

Enhanced perturbation measurement range chirped pulse ϕ-OTDR for distributed static measurements using feedback frequency-shift compensation technology

Chirped-pulse phase-sensitive optical time-domain reflectometry (CP-ϕOTDR) is a newly developed distributed optical fiber sensing (DOFS) technology that can measure physical quantities across the length of optical fiber. The measurement of perturbations as strain or temperature changes is performed by comparing the time domain trace of Rayleigh backscattered (RBS) light with a reference trace. However, large strain or temperature fluctuations or laser frequency noises necessitate frequent updates of the reference, and short-term change measurements must be integrated to obtain absolute strain or temperature, which introduces a 1/f noise component due to the combination of noise with each cumulative change. In this study, we propose a novel CP-ϕOTDR system based on frequency-shift compensation (FSC) technology. This system utilizes real-time feedback control of the central frequency of the incident laser to compensate for time domain trace distortion caused by large perturbations. It overcomes the limitations associated with the total chirp bandwidth and eliminates the 1/f noise component by obviating the need for frequent updates of the reference trace. The measuring range for perturbations is only limited to the wavelength range supported by the integrated optical devices, typically on the order of nanometers, corresponding to at least tens of Kelvin in temperature fluctuations or hundreds of micro-strains. To validate the effectiveness, we conducted a temperature measurement experiment involving a 1 K variation, which is about 90 times the maximum measurable perturbation that the original CP-ϕOTDR system can demodulate with the reference unchanged. The results demonstrate that the FSC method significantly expands the perturbation measurement range of the CP-ϕOTDR system.

Direct detection Φ-OTDR based on UWFBG array using linear-phase-modulated double-pulse

We propose what we believe to be a novel direct detection phase-sensitive optical time-domain reflectometry (Φ-OTDR) based on ultra-weak fiber Bragg grating (UWFBG) array to achieve distributed vibration measurements with exceptional sensitivity and remarkable stability. Our system employs a pulse modulator to generate a double pulse and achieves linear phase modulation of one pulse by one cycle through a phase modulator. The phase change can be quantitatively demodulated using our proposed N-step phase-shifted demodulation algorithm. This method effectively mitigates the influence of phase noise of the laser and the pulse modulator, while also eliminating fluctuations in the half-voltage of the phase modulator. Compared with the existing phase modulation methods, our method avoids stringent requirements for the stability and precision of phase modulation. Moreover, we propose a phase-shifted approximation method, breaking the limitation of sensing length on the traditional differential approximation method and improving the accuracy significantly. The technique's effectiveness is experimentally demonstrated on a 1 km UWFBG array with a reflectivity of −40 dB to −45 dB and a spatial resolution of 10 m. Vibrations with different amplitudes are measured quantitatively with good linearity. The low-frequency self-noise is greatly suppressed and the overall self-noise is −54.3 dB rad2/Hz.

A high-fidelity numerical model of coherent Φ-OTDR

The existing coherent phase-sensitive optical time-domain reflectometry (φ-OTDR) model does not systematically consider the impact of various disturbances and noises. More importantly, it cannot effectively simulate low-frequency vibrations. To address this issue, a high-fidelity model is proposed. Unlike the existing models, the proposed model comprehensively considers interference fading, polarization fading, the frequency drift and phase noise of the laser, and the balanced photodetector (BPD) noise, which can effectively simulate the impact of various disturbances and noises on the Rayleigh backscattering (RBS) signals during actual measurement. Quantitative analysis and comparison between the demodulation results of the measured and the numerically generated RBS signals based on the existing and proposed models with 1 kHz, 5 Hz and 1 Hz vibrations show that the proposed model is closer to the experimental system in terms of vibration demodulation results than the existing model.

Polar Decomposition of Jones Matrix and Mueller Matrix of Coherent Rayleigh Backscattering in Single-Mode Fibers.

The Jones matrix and the Mueller matrix of the coherent Rayleigh backscattering (RB) in single-mode fibers (SMFs) have been derived recently. It has been shown that both matrices depict two polarization effects-birefringence and polarization-dependent loss (PDL)-although the SMF under investigation is purely birefringent, having no PDL. In this paper, we aim to perform a theoretical analysis of both matrices using polar decomposition. The derived sub-Jones/Mueller matrices, representing birefringence and PDL, respectively, can be used to investigate the polarization properties of the coherent RB. As an application of the theoretical results, we use the derived formulas to investigate the polarization properties of the optical signals in phase-sensitive optical time-domain reflectometry (φ-OTDR). For the first time, to our knowledge, by using the derived birefringence-Jones matrix, the common optical phase of the optical signal in φ-OTDR is obtained as the function of the forward phase and birefringence distributions. By using the derived PDL-Mueller matrix, the optical intensity of the optical signal in φ-OTDR is obtained as the function of the forward phase and birefringence distributions as well as the input state of polarization (SOP). Further theoretical predictions show that, in φ-OTDR, the common optical phase depends on only the local birefringence in the first half of the fiber section, which is occupied by the sensing pulse, irrelevant of the input SOP. However, the intensity of the φ-OTDR signal is not a local parameter, which depends on the input SOP and the birefringence distribution along the entire fiber section before the optical pulse. Moreover, the PDL measured in φ-OTDR is theoretically proven to be a local parameter, which is determined by the local birefringence and local optical phase distributions.

Optical fiber vibration signal recognition based on an efficient multidimensional feature extraction network.

In the field of optical fiber vibration signal recognition, one-dimensional signals have few features. People often used the shallow layer of a one-dimensional convolutional neural network (1D-CNN), which results in fewer features being learned by the network, leading to a poor recognition rate. There are also many complex algorithms and data processing methods, which make the whole signal recognition process more complicated. Therefore, an optical vibration signal recognition method based on an efficient multidimensional feature extraction network was proposed. Based on ResNet-50, efficient channel attention (ECA) was used to improve image features extraction ability, and a long short-term memory (LSTM) network was used to enhance the extraction of temporal features. Three different vibration signals were collected using a phase-sensitive optical time-domain reflectometry (Φ-OTDR) optical fiber sensing system. Vibration signals were converted into 128×128 grayscale images, which have more effective vibration information. The experimental results show that the three types of signals can be recognized and classified effectively by the network, and the average recognition rate is 98.67%.

Novel Approach to Phase-Sensitive Optical Time-Domain Reflectometry Response Analysis with Machine Learning Methods.

This paper is dedicated to the investigation of the metrological properties of phase-sensitive reflectometric measurement systems, with a particular focus on addressing the non-uniformity of responses along optical fibers. The authors highlight challenges associated with the stochastic distribution of Rayleigh reflectors in fiber optic systems and propose a methodology for assessing response non-uniformity using both cross-correlation algorithms and machine learning approaches, using chirped-reflectometry as an example. The experimental process involves simulating deformation impact by altering the light source's wavelength and utilizing a chirped-reflectometer to estimate response non-uniformity. This paper also includes a comparison of results obtained from cross-correlation and neural network-based algorithms, revealing that the latter offers more than 34% improvement in accuracy when measuring phase differences. In conclusion, the study demonstrates how this methodology effectively evaluates response non-uniformity along different sections of optical fibers.

Sparrow search mechanism-based effective feature mining algorithm for the broken wire signal detection of prestressed concrete cylinder pipe

To enhance the capability of distributed optical fiber (DOF) monitoring system on real-time broken wire signal detection of prestressed concrete cylinder pipe (PCCP), this research proposes a novel effective parameter mining algorithm by utilizing wrapper theory and sparrow search algorithm (SSA). First, the improvement approach of SSA is investigated by applying the adaptive adjustment strategy, the Gaussian mutation perturbation, and the Tent chaotic perturbation. The subset search method is constructed based on the improved SSA (ISSA). Second, the similarity degrees of vibration signals are characterized by the Euclidean distance and the average linkage distance. On this basis, the subset evaluation criterion is established according to the classification accuracy of hierarchical clustering (HC). Finally, the prototype experiment is conducted in which the phase-sensitive optical time-domain reflectometry (Φ-OTDR) monitoring system is employed to collect vibration signals. Then, the effectiveness and the performance of the developed methodology are validated. The following results are drawn. (1) Benchmark function tests indicate that the convergence performance, the optimization ability, and the operation stability of ISSA are superior to those of SSA and the improved particle swarm optimization (IPSO). (2) The original set that has 122 feature parameters is established, and ISSA-HC, IPSO-HC, and SSA-HC extract 24, 31, and 55 effective parameters, respectively. (3) The recognition accuracies of the 3 parameter subsets are 95.83%, 84.17%, and 80.83%, respectively. The parameter subset mined by ISSA-HC has the best pattern recognition ability. This research provides technical support for the broken wire monitoring of in-service PCCP engineering. Meanwhile, it offers a foundation for further developing suitable pattern recognition algorithms to identify PCCP broken wire signals.

A Pattern Recognition Method for Filter Bags in Bag Dust Collectors Based on Φ-Optical Time-Domain Reflectometry

The use of phase-sensitive optical time-domain reflectometry (Φ-OTDR)-distributed fiber vibration sensors to detect and identify damaged bags in bag dust collectors has the potential to overcome the inadequacy of traditional damaged bag detection methods. In our previous study, we verified the feasibility of applying this technique in the field of damaged bag detection in bag filters. However, many problems still occur in engineering applications when using this technology to detect and identify damaged filter bags in pulse-jet dust-cleaning bag dust collectors. Further studies are needed to characterize the fiber vibration signals inside different types of rectangular damaged filter bags. A filter bag damage identification and detection method based on empirical mode decomposition (EMD) and a backpropagation (BP) neural network is proposed. The signal feature differences between intact filter bags and damaged filter bags with different rectangular hole sizes and positions are comparatively analyzed, and optimal feature difference parameters are proposed. Support vector machine (SVM) and a BP neural network are used to recognize different types of filter bag signals, and the comparison results show that the BP neural network algorithm is better at recognizing different types of filter bags, obtaining the highest recognition rate of 97.3%.

Ameliorated 3 × 3 coupler-based demodulation algorithm using iteratively reweighted ellipse specific fitting.

Passive demodulation scheme using 3 × 3 coupler has been widely used in phase-sensitive optical time-domain reflectometry (φ-OTDR), interrogation of fiber Bragg gratings or fiber optic interferometric sensors, and sensor multiplexing. However, the asymmetry of the 3 × 3 coupler in real applications affects the demodulation performance seriously. We proposed an ameliorated 3 × 3 coupler-based demodulation algorithm using iteratively reweighted ellipse specific fitting (IRESF) to overcome the drawback. IRESF combines iterative reweight technology with ellipse specific fitting, which decreases the weights of high noise points and always outputs ellipse solutions. Any two output signals from the 3 × 3 coupler-based interferometer are fitted by the IRESF and then corrected as a pair of quadrature signals. The stability of the fitting parameters is utilized to resolve the failures of IRESF under small signals. A real-time 1/4 ellipse arc judging module is designed, if the Lissajous figure is larger than 1/4 ellipse arc, IRESF is executed to offer ellipse correction parameters. Otherwise, the fixed parameters preset in the algorithm are used. The fixed parameters are mean values of the fitting parameters of IRESF under a large stimulus. The desired phase signal is finally extracted from the corrected quadrature signals. Experimental results show that the ameliorated algorithm does not require strict symmetry of the 3 × 3 coupler and can work under small signals. The noise floor of the proposed algorithm is -112 dB re rad/√Hz and the demodulated amplitude is 23.15 dB (14.37 rad) at 1 kHz when THD is 0.0488%. Moreover, the response linearity is as high as 99.999%. Compared to the algorithm using direct least squares, the proposed demodulation algorithm is more robust and precise, which has broad application prospects.

Experimental research on signal sensing of distributed acoustic sensing optical fiber based on Φ-OTDR in shallow water

The distributed acoustic sensing (DAS) technology based on phase-sensitive optical time domain reflectometry (Φ-OTDR) has been widely and well applied on land. Meanwhile, the distributed acoustic sensing optical fiber (DASF) based on Φ-OTDR, as a new type of sensor, is still in the initial stage of research, gets much attention in recent years because of its long-distance monitoring, high sensitivity, anti-electromagnetic interference, good concealment and so on. Performances of DASF in sensing different kinds of acoustic source signals were given by experiments in shallow water, which included single frequency pulse signal in the fixed position, the moving single frequency continuous pulse signal, the moving broadband signal, and the signal generated by the sailing surface target. Depth of the active sound source was changed to examine the performance of DASF in sensing signals in different depths. The specific experimental process was given, and the experimental results were processed and analyzed. Additionally, wave components of data generated from of the sailing surface vessel were explored with the time delay difference between the equivalent array elements of DASF. Results show that DASF can sense the above varieties of underwater acoustic signals well, and the physical field generated by the sailing surface vessel in shallow water include acoustic field and flow field, in which the propagation velocity of the former is faster than that of the latter.

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.

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.

Acceleration of the frequency-shift demodulation in phase-sensitive OTDR

Abstract The frequency-shift demodulation is a primary demodulation method in phase-sensitive optical time domain reflectometry (Φ-OTDR) with intrinsic resistance to interference fading. So far, the least mean squares (LMS) estimation method has the optimal demodulation accuracy and robustness. However, it takes much processing time due to the step-by-step sliding operation. In this work, we propose a fast LMS estimation method based on cross-correlation calculation to accelerate the demodulation while maintaining accuracy. Experiments are performed along a 9 km sensing fiber with a 4 m spatial resolution. The performance of the fast LMS, LMS, and cross-correlation methods are compared by using the same parameters. Compared with the LMS method, the fast LMS achieves a 12-time improvement in processing speed while remaining the same demodulation accuracy. Although the proposed fast LMS method takes slightly more time than the cross-correlation method (1.6 times), it improves the demodulation accuracy ∼6 dB for the vibration signal and ∼2.1 dB for the overall demodulation accuracy.

A spatial and temporal signal fusion based intelligent event recognition method for buried fiber distributed sensing system

Due to the characteristics of high sensitivity, fast response speed and multi-point monitoring, Phase-sensitive optical time-domain reflectometry (Φ-OTDR) has attracted attention in the field of perimeter security. However, the intrusion signals are susceptible to interference by ambient signals and difficult to be recognized. In the field of the vibration event recognition of Φ-OTDR system, the deep-learning based methods achieve great recognition ability. However, the previous works are mostly built on single signal source like temporal signal and may not completely adapt to different environment. In this work, a novel phenomenon is reported that a specific variation pattern of light intensity, which is related to the type of vibration source, is hided in the backscattering traces in spatial domain. Inspired by this, a lightweight model and data composition method is proposed to fuse spatial information with temporal correlation information based on end-to-end CNN-LSTM combined model and bicubic scaling. The experiment is conducted on a portable computer (a Nvidia GPU RTX 2080 with 2944 compute unified device architecture cores) with 8 event types and shows that this method can achieve 95.56% validation accuracy through less than 6 min training. Compared with previous method trained by single image structure signal, this lightweight work can achieve higher validation accuracy faster.

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.

Intelligent water perimeter security event recognition based on NAM-MAE and distributed optic fiber acoustic sensing system.

Distributed optical acoustic sensing (DAS) based on phase-sensitive optical time-domain reflectometry can realize the distributed monitoring of multi-point disturbances along an optical fiber, thus making it suitable for water perimeter security applications. However, owing to the complex environment and the production of various noises by the system, continuous and effective recognition of disturbance signals becomes difficult. In this study, we propose a Noise Adaptive Mask-Masked Autoencoders (NAM-MAE) algorithm based on the novel mask mode of a Masked Autoencoders (MAE) and applies it to the intelligent event recognition in DAS. In this method, fewer but more accurate features are fed into the deep learning model for recognition by directly shielding the noise. Taking the fading noise generated by the system as an example, data on water perimeter security events collected in DAS underwater acoustic experiments are used. The NAM-MAE is compared with other models. The results indicate higher training accuracy and higher convergence speed of NAM-MAE than other models. Further, the final test accuracy reaches 96.6134%. It can be demonstrated that the proposed method has feasibility and superiority.

SNR enhancement of quasi-distributed weak acoustic signal detection by elastomers and MMF integrated Φ-OTDR.

We have proposed and demonstrated a weak acoustic signal detection technology based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). Non-contact acoustic signals transmitting through air gap between the sound source and the receiver are difficult to detect due to fast attenuation. In order to improve the detection ability of non-contact weak acoustic signals, we demonstrate that multi-mode fiber (MMF) is a better solution than single-mode fiber (SMF) benefiting from its larger core and higher Rayleigh backscattering (RBS) capture coefficient. The frequency signal-to-noise ratio (SNR) has been enhanced by 9.26 dB. Then, with the help of 3D printing technology, elastomers have been designed to further enhance the detection ability due to the high-sensitive response to acoustic signals. Compared with the previous reported "I" type elastomer, the location and frequency SNR enhancement caused by our new proposed "n" type elastomer are 8.39 dB and 11.02 dB in SMF based system. The values are further improved to 10.51 dB and 13.38 dB in MMF and "n" type elastomer integrated system. And a phase-pressure sensitivity of -94.62 dB re rad/µPa has been achieved at 2.5 kHz. This non-contact weak acoustic signal detection technique has great application potential in the quasi-distributed partial discharge (PD) detection of smart grid.