Search Results (61)

The phase-sensitive optical time-domain reflectometry (Φ-OTDR) system has shown substantial potential in distributed acoustic sensing applications. Accurate event classification is crucial for effective deployment of Φ-OTDR systems, and various methods have been proposed for event classification in Φ-OTDR systems. However, most existing methods typically rely on sufficient labeled signal data for model training, which poses a major bottleneck in applying these methods due to the expensive and laborious process of labeling extensive data. To address this limitation, we propose CLWTNet, a novel contrastive representation learning method enhanced with wavelet transform convolution for event classification in Φ-OTDR systems. CLWTNet learns robust and discriminative representations directly from unlabeled signal data by transforming time-domain signals into STFT images and employing contrastive learning to maximize inter-class separation while preserving intra-class similarity. Furthermore, CLWTNet incorporates wavelet transform convolution to enhance its capacity to capture intricate features of event signals. The experimental results demonstrate that CLWTNet achieves competitive performance with the supervised representation learning methods and superior performance to unsupervised representation learning methods, even when training with unlabeled signal data. These findings highlight the effectiveness of CLWTNet in extracting discriminative representations without relying on labeled data, thereby enhancing data efficiency and reducing the costs and effort involved in extensive data labeling in practical Φ-OTDR system applications. Full article

To address the high false alarm rate of intrusion detection systems based on distributed acoustic sensing (DAS) for power cables in complex underground environments, an innovative GRT-Transformer multimodal deep learning model is proposed. The core of this model lies in its distinctive three-branch parallel collaborative architecture: two branches employ Gramian Angular Summation Field (GASF) and Recursive Pattern (RP) algorithms to convert one-dimensional intrusion waveforms into two-dimensional images, thereby capturing rich spatial patterns and dynamic characteristics and the third branch utilizes a Gated Recurrent Unit (GRU) algorithm to directly focus on the temporal evolution features of the waveform; additionally, a Transformer component is integrated to capture the overall trend and global dependencies of the signals. Ultimately, the terminal employs a Bidirectional Long Short-Term Memory (BiLSTM) network to perform a deep fusion of the multidimensional features extracted from the three branches, enabling a comprehensive understanding of the bidirectional temporal dependencies within the data. Experimental validation demonstrates that the GRT-Transformer achieves an average recognition accuracy of 97.3% across three typical intrusion events—illegal tapping, mechanical operations, and vehicle passage—significantly reducing false alarms, surpassing traditional methods, and exhibiting strong practical potential in complex real-world scenarios. Full article

This paper presents hardware-efficient phase demodulation schemes for FPGA-based digital phase-sensitive optical time-domain reflectometry ($\varphi$-OTDR) receivers. We first derive a signal model for the heterodyne $\varphi$-OTDR frontend, then propose and analyze three demodulation methods: (1) a baseband reconstruction approach via zero-IF downconversion, (2) an analytic signal generation technique using the Hilbert transform (HT), and (3) a wavelet transform (WT)-based alternative for analytic signal extraction. Algorithm-hardware co-design implementations are detailed for both RFSoC and conventional FPGA platforms, with resource utilization comparisons. Additionally, we introduce an incremental DC-rejected phase unwrapper (IDRPU) algorithm to jointly address phase unwrapping and DC drift removal, minimizing computational overhead while avoiding numerical overflow. Experiments on simulated and real-world $\varphi$-OTDR data show that the HT method matches the performance of zero-IF demodulation with simpler hardware and lower resource usage, while the WT method offers enhanced robustness against fading noise (3.35–22.47 dB SNR improvement in fading conditions), albeit with slightly ambiguous event boundaries and higher hardware utilization. These findings provide actionable insights for demodulator design in distributed acoustic sensing (DAS) applications and advance the development of single-chip DAS systems. Full article

The phase-sensitive optical time domain reflectometry (Φ-OTDR) has been widely applied in various fields. However, due to fading noise, false alarms often occur; this limits its engineering applications. In this paper, a fading suppression method for Φ-OTDR systems based on multi-domain multiplexing (MDM) is proposed. The principles and limitations of existing suppression methods such as spatial-domain multiplexing (SDM), polarization-domain multiplexing (PDM), and frequency-domain multiplexing (FDM) are analyzed. The principle of MDM is explained in detail, and an experimental system is established to test the fading noise suppression capabilities of different parameter combinations of the PDM, FDM, and SDM methods. Experimental results show that it is difficult to comprehensively suppress fading noise with single-domain multiplexing. Through optimizations of different parameter combinations, MDM can comprehensively suppress fading noise by appropriately selecting the number of FDM frequencies, the spatial weighting intervals, and using PDM, thus obtaining the optimal anti-fading solution between performance and hardware complexity. Through MDM, the fade-free measurement is achieved, providing a promising technical solution for the practical application of the Φ-OTDR technology. Full article

In this paper, a digitalized polarization fading suppression and phase demodulation technique for a phase-sensitive optical time-domain reflectometry (φ-OTDR) sensing system utilizing polarization diversity virtual coherence is proposed, in which virtual cross-coherence between the polarization diversity digital signals is employed for simultaneous fading noise suppression and phase demodulation. The principle of the proposed demodulation algorithm is presented and analyzed. Based on this, the practicability and validity of the proposed demodulation method for fading noise suppression and distributed vibration sensing are confirmed through experiments. The experimental results indicate that the proposed demodulation scheme can effectively reduce the polarization fading noise caused by the polarization mismatch between the probe light and the reference light, and the phase changes induced by external interference can also be accurately recovered with a signal-to-noise ratio (SNR) of vibration signal localization of 27.14 dB and an SNR of vibration signal phase demodulation of 47.88 dB, which provides a simplified method for simultaneous polarization fading suppression and the phase demodulation of the φ-OTDR system. Full article

The phase-sensitive optical time domain reflectometry (Φ-OTDR) technique offers a method for distributed acoustic sensing (DAS) systems to detect external acoustic fluctuations and mechanical vibrations. By accurately identifying vibration events, DAS systems provide a non-invasive solution for security monitoring. However, limitations in temporal signal analysis and the lack of spatial features significantly impact classification accuracy in event recognition. To address these challenges, this paper proposes a network model for vibration-event recognition that integrates convolutional neural networks (CNNs), bidirectional gated recurrent units (BiGRUs), and attention mechanisms, referred to as CNN-BiGRU-Attention (CBA). First, the CBA model processes spatiotemporal matrices converted from raw signals, extracting low-level features through convolution and pooling. Subsequently, features are further extracted and separated along both the temporal and spatial dimensions. In the spatial-dimension branch, horizontal convolution and pooling generate enhanced spatial feature maps. In the temporal-dimension branch, vertical convolution and pooling are followed by BiGRU processing to capture dynamic changes in vibration events from both past and future contexts. Additionally, the attention mechanism focuses on extracted features in both dimensions. The features from the two dimensions are then fused using two cross-attention mechanisms. Finally, classification probabilities are output through a fully connected layer and a softmax activation function. In the experimental simulation section, the model is validated using real-world data. A comparison with four other typical models demonstrates that the proposed CBA model offers significant advantages in both recognition accuracy and robustness. Full article

Distributed Acoustic Sensing (DAS) has emerged as a groundbreaking technology in seismology, transforming fiber-optic cables into dense, cost-effective seismic monitoring arrays. DAS makes use of Rayleigh backscattering to detect and measure dynamic strain and vibrations over extended distances. It can operate using both pre-existing telecommunication networks and specially designed fibers. This review explores the principles of DAS, including Coherent Optical Time Domain Reflectometry (COTDR) and Phase-Sensitive OTDR (ϕ-OTDR), and discusses the role of optoelectronic interrogators in data acquisition. It examines recent advancements in fiber design, such as helically wound and engineered fibers, which improve DAS sensitivity, spatial resolution, and the signal-to-noise ratio (SNR). Additionally, innovations in deployment techniques include cemented borehole cables, flexible liners, and weighted surface coupling to further enhance mechanical coupling and data accuracy. This review also demonstrated the applications of DAS across earthquake detection, microseismic monitoring, reservoir characterization and monitoring, carbon storage sites, geothermal reservoirs, marine environments, and urban infrastructure surveillance. The study highlighted several challenges of DAS, including directional sensitivity limitations, vast data volumes, and calibration inconsistencies. It also addressed solutions to these problems, such as advances in signal processing, noise suppression techniques, and machine learning integration, which have improved real-time analysis and data interpretability, enabling DAS to compete with traditional seismic networks. Additionally, modeling approaches such as full waveform inversion and forward simulations provide valuable insights into subsurface dynamics and fracture monitoring. This review highlights DAS’s potential to revolutionize seismic monitoring through its scalability, cost-efficiency, and adaptability to diverse applications while identifying future research directions to address its limitations and expand its capabilities. Full article

Optimizing the use of existing high-voltage transmission lines demands real-time condition monitoring to ensure structural integrity and continuous service. Operating these lines at the current capacity is limited by safety margins based on worst-case weather scenarios, as exceeding these margins risks bringing conductors dangerously close to the ground. The integration of optical fibers within modern transmission lines enables the use of Distributed Fiber Optic Sensing (DFOS) technology, with Chirped-Pulse Phase-Sensitive Optical Time-Domain Reflectometry (CP-$\mathrm{\Phi }$OTDR) proving especially effective for this purpose. CP-$\mathrm{\Phi }$OTDR measures wind-induced vibrations along the conductor, allowing for an analysis of frequency-domain vibration modes that correlate with conductor length and sag across spans. This monitoring system, capable of covering distances up to 40 km from a single endpoint, enables dynamic capacity adjustments for optimized line efficiency. Beyond sag monitoring, CP-$\mathrm{\Phi }$OTDR provides robust detection of external threats, including environmental interference and mechanical intrusions, which could compromise cable stability. By simultaneously monitoring the Optical Phase Conductor (OPPC) and Optical Ground Wire (OPGW), this study offers the first comprehensive, real-time evaluation of both structural integrity and potential external aggressions on overhead transmission lines. The findings demonstrate that high-frequency data offer valuable insights for classifying mechanical intrusions and environmental interferences based on spectral content, while low-frequency data reveal the diurnal temperature-induced sag evolution, with distinct amplitude responses for each cable. These results affirm CP-$\mathrm{\Phi }$OTDR’s unique capacity to enhance line safety, operational efficiency, and proactive maintenance by delivering precise, real-time assessments of both structural integrity and external threats. Full article

Phase-sensitive optical time domain reflectometry (Φ-OTDR) plays a crucial role in localizing and monitoring seismic waves, underwater structures, etc. Accurate localization of external perturbations along the fiber is essential for addressing these challenges effectively. The conversion coefficient, which links the detected phase signal to the perturbation signal on the fiber, has a significant impact on localization accuracy. This makes the characteristic of parameters relative to the conversion coefficient in Φ-OTDR a subject of deep research. Based on the coherent Φ-OTDR mathematical model, parameters like the modulus, the statistical phase, the phase change, and the peak difference are analyzed with and without the static region, respectively. When perturbations are homogeneously distributed along the fiber, the absence of static region on the phase change-fiber length plane leads to a nonlinear phase change relationship. This deviation from the expected linear relationship in the presence of static region means that the static region is essential for higher localization accuracy. The absence of static region results in a standard deviation of 0.042263 m for the localization deviation value, which could be theoretically reduced by a new sensor design with a static region. These findings underscore the importance of the conversion coefficient and the relevance of the static region in Φ-OTDR to achieving accurate and effective localization. Full article

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. Full article

The large amount of sampled data in coherent phase-sensitive optical time-domain reflectometry (Φ-OTDR) brings heavy data transmission, processing, and storage burdens. By using the comparator combined with undersampling, we achieve simultaneous reduction of sampling rate and sampling resolution in hardware, thus greatly decreasing the sampled data volume. But this way will inevitably cause the deterioration of detection signal-to-noise ratio (SNR) due to the quantization noise’s dramatic increase. To address this problem, denoising the demodulated phase signals using compressed sensing, which exploits the sparsity of spectrally sparse vibration, is proposed, thereby effectively enhancing the detection SNR. In experiments, the comparator with a sampling parameter of 62.5 MS/s and 1 bit successfully captures the 80 MHz beat signal, where the sampled data volume per second is only 7.45 MB. Then, when the piezoelectric transducer’s driving voltage is 1 Vpp, 300 mVpp, and 100 mVpp respectively, the SNRs of the reconstructed 200 Hz sinusoidal signals are respectively enhanced by 23.7 dB, 26.1 dB, and 28.7 dB by using compressed sensing. Moreover, multi-frequency vibrations can also be accurately reconstructed with a high SNR. Therefore, the proposed technique can effectively enhance the system’s performance while greatly reducing its hardware burden. Full article

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. Full article

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. Full article

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%. Full article

The choice of wavelet decomposition layer (DL) not only affects the denoising quality of wavelet denoising (WD), but also limits the denoising efficiency, especially when dealing with real phase-sensitive optical time-domain reflectometry ($\phi$-OTDR) signals with complex signal characteristics and different noise distributions. In this paper, a straightforward adaptive DL selection method is introduced, which dose not require known noise and clean signals, but relies on the similarity between the probability density function (PDF) of method noise (MN) and the PDF of Gaussian white noise. Validation is carried out using hypothetical noise signals and measured $\phi$-OTDR vibration signals by comparison with conventional metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The proposed wavelet DL selection method contributes to the fast processing of distributed fiber optic sensing signals and further improves the system performance. Full article