Phase-sensitive Optical Time-domain Research Articles

Distributed fiber optic sensing signal recognition based on class-incremental learning

Distributed fiber optic sensing (DFOS) based on phase-sensitive optical time-domain reflectance (φ-OTDR) technology has outstanding performance in pipeline safety monitoring and perimeter security detection. Accurate identification of new events remains challenging due to environmental variability and emerging forms of intrusions. In order to solve the problem of failing to accurately identify new events due to the inability to obtain all samples at once in real-time monitoring, this paper proposes an incremental learning network framework for distributed fiber-optic sensing signal recognition. This framework integrates an optimized Learning without Memorizing (LwM) algorithm with an improved ConvNeXt network for dynamic training of new events. An improved Efficient Channel Attention (HECA) is incorporated to thoroughly extract the spatio-temporal features of the intrusion signals collected by the DFOS. The forgetting problem is mitigated during incremental learning using knowledge distillation and optimized Gradient Weighted Class Activation Mapping to generate an attention map. A linear correction layer is added after the output layer to correct the bias towards new classes by rebalancing the information between new and old classes. Experimental comparisons show that the recognition rate for 10 different intrusion signals exceeds 93 %, while the forgetting rate is reduced from a peak of 41.44 % to 5.25 %. The time required to process and train incremental learning for 1000 samples in real time on an edge device (NVIDIA 3050 GPU) is approximately 1060 s, its ability to demonstrating its suitability for deployment in resource-constrained.

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.

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.

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.

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.

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.

Voiceprint and position information detection of non-cooperative ship with Φ-OTDR and suspended sensitized optical cable

Target detection is significant in many fields, including oceanic security, marine ecology, etc. In this paper, phase sensitive optical time domain reflectometry (Φ-OTDR) is introduced for the non-cooperative ship detection, with large-scale diversity technology and suspended sensitized optical cable. In outfield experiments, the ship’s voiceprint information is obtained in high fidelity, the ship’s power spectrum is analyzed, and the over-top detection is achieved. Moreover, an array orientation method based on adaptive phase difference correction (APDC) is proposed to track the ship, suppressing the delay jitter influence of acoustic transmission underwater. This is the first time that voiceprint information of the non-cooperative ship is high-fidelity acquired and deeply analyzed with Φ-OTDR and suspended sensitized optical cable, which is conducive to the detection and identification of marine targets, and proves the potential of Φ-OTDR in hydroacoustic detection applications.

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.

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.

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%.

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.

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.

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%.