Search Results (243)

Distributed optical fiber sensing (DOFS) based on phase-sensitive optical time-domain reflectometry (ϕ-OTDR) is increasingly used for perimeter and pipeline monitoring, yet most recognizers are evaluated as closed-set classifiers, whereas a deployed fiber also records nuisance sources and event types absent from training. In our experiments, a closed-set recognizer with 0.999 accuracy still gives a false-alarm rate (FAR) of 0.42–0.64 on simulated unknown nuisance classes, and a conformal threshold calibrated only on known negatives does not remove this failure. We therefore propose Physically Consistent Risk Calibration (PCRC), which combines a label-free physical-consistency gate computed from spatial compactness, common-mode ratio, and signal-to-noise ratio (SNR) with a conformal threshold for gate-passing negatives and a three-level alarm/review/discard decision. The guarantee is conditional: conformal calibration controls gate-passing negatives under exchangeability, and the gate removes only physically inadmissible nuisance windows. On three public distributed acoustic sensing (DAS) field datasets, the known-negative FAR follows the target level when calibration and test negatives are exchangeable, held-out threat coverage reaches 0.87 and 0.68 on the two multichannel corpora, and every physically admissible held-out nuisance passes the gate and requires recalibration from verified field negatives. In a controlled simulation whose unknown nuisance classes are constructed to be inadmissible, PCRC produces no observed automatic false alarms while retaining 0.998 known-threat recall. Full article

Accurate tracking and localization of in-line inspection (ILI) tools are essential for mileage calibration, defect mapping, and blockage prevention in oil and gas pipelines. This review summarizes sensor-based approaches for external ILI-tool localization, emphasizing how sensing physics, deployment geometry, and signal interpretation determine practical performance. Extremely low-frequency (ELF) magnetic tracking is first examined through dipole modeling, sensor evolution, and weak-signal recovery under steel-pipe and soil shielding. Distributed fiber-optic sensing is then reviewed as a continuous-tracking alternative, with attention to fading mitigation, spatiotemporal denoising, and trajectory extraction from distributed acoustic sensing data. Acoustic arrays and hybrid schemes are discussed as complementary options for subsea or cable-free environments. Finally, the review assesses how data fusion and lightweight artificial intelligence (AI) can improve robustness while noting unresolved issues in field data availability, edge computing, and uncertainty quantification. The synthesis indicates that next-generation ILI tracking should combine heterogeneous sensing, physics-aware signal processing, and deployment-aware model design rather than rely on a single high-sensitivity sensor. Full article

In the field of long-distance partial discharge (PD) detection for submarine cables, there is an urgent need for a remote demodulation distributed detection technology deployable at multiple critical locations to overcome the limitations of single-point measurement. Factory joints of high-voltage submarine cables are high-risk components for PD, and long-distance fiber optic acoustic sensing technology holds the greatest potential for online monitoring. However, due to the viscoelasticity of the joint’s polymer insulation structure, sound propagation distance is severely limited, and non-multi-point measurement cannot achieve effective coverage of the measurement area. This paper proposes a quasi-distributed remote demodulation sensing system, in which both the fiber optic interferometer and the distributed feedback fiber laser (DFB-FL) serve as sensors, enabling highly sensitive quasi-distributed PD detection. The DFB-FL itself is highly sensitive to strain, and the multiple fiber coils formed by the interferometer arms are also highly sensitive to strain. Both can be modulated by the micro-strain induced by the acoustic field generated from PD in the polymer solid, producing phase shifts of the optical waves, which are then intrinsically demodulated by the interferometer system to extract the vibration signals caused by the discharge. Theoretical analysis shows that the sensitivity increases with the length of the unbalanced arm, with the upper limit constrained by laser coherence and optical attenuation; for the PD frequency band, the optimal unbalanced length is below 200 m—this design rule is applicable to on-chip interferometric sensors. The interferometer coils employ bend-insensitive fibers to suppress optical loss and improve fringe visibility. Meanwhile, three fiber coil configuration schemes are constructed to enhance the detection sensitivity to acoustic signals. Simulation results generate frequency response contour maps based on Young’s modulus, indicating that the solid-wound coil achieves the highest amplitude and the broadest bandwidth, with an optimal response frequency of approximately 50 kHz. Experimental results demonstrate that among the three structure types, the solid-wound coil also achieves the largest response ratio. Finally, in tests performed on a 220 kV submarine cable intermediate joint (with the system installed inside the metallic sheath), the minimum detectable discharge level in the DFB-FL region reached 6.75 pC, while that for the fiber coil reached 12.66 pC; when installed outside the metallic sheath, the minimum detectable discharge levels were 53.2 pC for the grating region and 89.6 pC for the fiber coil. Full article

Distributed Acoustic Sensing (DAS) provides dense spatial measurements of the dynamic strain along fiber optic cables, offering high-resolution wave sensing for ground motion monitoring and subsurface imaging applications. However, DAS records the axial strain or strain rate, whereas traditional seismic and engineering ground motion equipment and derived metrics are based on particle displacement, velocity, or acceleration, necessitating reliable strain-to-velocity conversion methods. This study evaluates three widely used conversion approaches: the fk-rescaling, curvelet-based conversion, and slant-stack methods. These approaches are applied to a unique high-energy, near-field, active-source dataset collected at the Birds Landing Site in Sherman Island, California. The dataset includes wavefields generated by a large transmission tower collapse and sledgehammer impacts used for subsurface imaging. The wavefields were recorded simultaneously by a 1.4 km DAS array and 71 collocated nodal stations (NSs). Using 63 DAS–NS pairs, we quantify the strain-to-velocity conversion method performance using amplitude and phase transfer functions (TFs) between DAS-derived and NS particle velocity records, with the root-mean-square error (RMSE) evaluated across three frequency bands: 0.5–100 Hz, 1–10 Hz, and 10–100 Hz. The results show that fk-rescaling provides the most stable amplitude response across both source types, while both the fk-rescaling and slant-stack methods generally yield the best phase agreement. Curvelet-based conversion shows a greater variability and larger RMSE values. All methods yield a poorer amplitude reconstruction at higher frequencies, while the phase content is generally preserved more reliably than amplitudes. Differences between the tower collapse and sledgehammer sources demonstrate the influence of the source characteristics and spatial processing window length on the conversion performance. The findings provide practical guidance for selecting suitable strain-to-velocity conversion methods for active-source DAS applications, particularly where collocated reference sensors are unavailable. Full article

Distributed acoustic sensing (DAS) systems capture complex, nonlinear wave propagation across fiber-optic cables. Conventional event classification architectures, constrained by static physical topologies or isolated spatial grids, fail to effectively adapt to the dynamic feature relationships associated with such events, particularly when modeling complex spatiotemporal interactions across sensor arrays. To resolve these structural limitations, we introduce the Spectro-Spatial Dynamic Graph Neural Network (S2-DyGNN), whose architecture couples a two-dimensional frequency–time convolutional front-end with a dual-matrix graph neural network (GNN). First, the convolutional module extracts spectro-temporal features, explicitly capturing localized acoustic dynamics independent of inter-sensor interference. Subsequently, the graph module constructs a dual-matrix topology, fusing a static physical distance prior with a data-driven adjacency matrix that recalculates spatial connections frame by frame from input signals. When evaluated on a highly skewed nine-class DAS field dataset, S2-DyGNN outperformed other conventional models by achieving a peak macro-averaged F1-score of 86.6% and an overall accuracy of 94.0%. The dual-matrix graph topology prevented dominant background features from washing out sparse transient events, improving the minority “openclose” class F1-score to 55.7% compared to the 48.0% ceiling of a static graph topology. These results demonstrate that explicitly coupling localized spectro-temporal representations with physically anchored spatial topologies consistently outperforms models that process these domains in isolation, providing a highly robust and scalable solution for real-world continuous monitoring systems. Full article

Intelligent completion technologies, including Inflow Control Valves (ICVs), have become increasingly important for remotely managing zonal production in complex well architectures. However, quantifying flow rates and phase fractions in such systems remains challenging due to space constraints and the harsh downhole environment, which limit the deployment of conventional sensors. Distributed Acoustic Sensing (DAS) provides a promising solution by converting standard fiber-optic cables into dense arrays of acoustic sensors. While DAS has been successfully applied in applications such as integrity monitoring and leak detection, its use for direct two-phase flow characterization within intelligent completions remains largely unexplored. In this study, we present a DAS-based methodology to monitor and analyze oil–water two-phase flow in horizontal experiments that mimic field conditions. Acoustic data collected from DAS are transformed into time–frequency spectrograms using Short-Time Fourier Transform (STFT) to extract dynamic spectral features. These features are then correlated with pressure drop across the ICV and flow rate, revealing distinct frequency band behaviors associated with fluid changes. To quantify flow characteristics, a power-law model is trained using spectral features to predict flow rate and phase fractions. The results demonstrate strong predictive capability for pressure drop and flow rate under controlled laboratory conditions, highlighting the potential of DAS for multiphase flow diagnostics in field applications with intelligent completions, while water cut prediction remains challenging due to the complex and non-unique relationship between flow conditions and DAS response and is left for future work. This research not only provides new insights into the acoustic response of oil–water flows but also introduces a data-driven framework for leveraging DAS in real-time flow monitoring and control within ICV-equipped completions. Full article

To address the limited spatial localization accuracy of partial discharge (PD) in high-voltage cable terminations and the difficulty in accurately determining the trigger time in traditional ultrasonic detection, this paper proposes an electro-acoustic synergistic localization technology based on a high-frequency current transformer (HFCT) and a Sagnac optical fiber interferometer. A high-sensitivity Sagnac acoustic sensor based on a 3D-printed photosensitive resin mandrel was developed. Through structural design and 0–50 kHz amplitude–frequency testing, the sensor exhibits a dominant resonant response at 33.2 kHz. This narrow-band, high-sensitivity characteristic effectively enhances the perception capability for weak PD ultrasonic signals. An electro-acoustic synergistic detection system was constructed, in which the high-frequency PD current signal captured by the HFCT was used as the electrical time reference, and a dual-channel Sagnac sensor array was used to extract the arrival times of ultrasonic waves. In a 12 kV laboratory cable-termination PD experiment, the proposed system identified the representative built-in air-gap PD source with an absolute localization error of 5 mm under the tested laboratory configuration. This value should be interpreted as the localization result for the tested representative defect, rather than as a generally validated accuracy specification of the system. This study provides a proof-of-concept laboratory demonstration of an electro-acoustic localization strategy that combines the fast electrical response of HFCT detection with the electromagnetic-interference immunity and acoustic sensitivity of Sagnac fiber-optic sensing. Full article

The reliable evaluation of the interfacial bonding quality of steel structures strengthened with carbon fiber-reinforced polymer (CFRP) is crucial to ensuring the long-term service safety of the structures. Focusing on the active and passive detection methods based on piezoelectric sensing, this paper takes numerical simulation as the core research method to provide theoretical verification and mechanism explanation for subsequent key experiments, thus supporting the accurate detection of interfacial damage in CFRP–steel plate joints. A 3D piezoelectric–structural coupling finite element model and a 2D ultrasonic guided wave propagation finite element model were established via COMSOL Multiphysics 6.2 to systematically simulate the electromechanical response characteristics of three piezoelectric sensors (PMN-PT, PZT and PVDF). The research focused on analyzing the potential output and voltage–load response of the three sensors, and simultaneously explored the propagation laws and energy evolution mechanisms of ultrasonic waves in the presence of different debonding damages and groove defects in CFRP plates. The simulation results show that the PMN-PT sensor exhibits the optimal detection performance, with its peak potential output reaching 2.66 times that of the PZT sensor and 4.69 times that of the PVDF sensor, with a load sensitivity of 484.3 mV/kN. In the ultrasonic active detection of interfacial debonding damage, the first-wave amplitude has a significant positive correlation with the debonding length, and this characteristic is attributed to the strong reflection effect and energy accumulation caused by the acoustic impedance mismatch at the CFRP–air interface. For the internal groove defects in CFRP plates, the simulation clarifies that the increase in groove length leads to energy trapping in the plate, while the increase in groove depth intensifies ultrasonic wave energy reflection. The numerical simulation results were compared and verified with data from companion experiments conducted by the authors’ team, showing a high degree of consistency, which confirms the accuracy and reliability of the established finite element models. Meanwhile, the physical essence of damage detection is elucidated from the perspective of wave theory, providing a solid numerical analysis foundation and theoretical support for the intelligent monitoring of interfacial damage in CFRP–steel structures. Full article

The Garisenda Tower, along with the neighboring Asinelli Tower, is arguably the symbol of the city of Bologna. They are the sole remnants of about one hundred towers that formed the city’s skyline in medieval times. As such, the monitoring of their state of health has been of great interest to the scientific community for more than a century—one example being the studies of Prof. Cavani in the early 1900s. The Garisenda Tower, famous for its impressive lean, is the object of Structural Health Monitoring (SHM) involving a multitude of devices. Some examples are a 30 m long pendulum installed on the inside of the tower to measure the planar displacement of the tower’s top; Fiber-Optical Strings (FOSs) installed in the walls of the basement to measure their vertical deformation; and piezoelectric acoustic emission (AE) sensors, also installed on the walls of the tower’s basement to detect elastic waves generated by micro-cracking. This rich experimental setup allows for the investigation of the tower’s stability and damage assessment. In this work, attention is focused on two analyses: The first is a Principal Component Analysis (PCA) study that investigates the correlation between AE data and other SHM data, such as in situ temperature, pendulum displacement, and AE rate. The second analysis corresponds with numerical finite element (FE) studies that assess damage in the base of the tower. Initially, the Smeared Cracking material model is used to understand which zones of the tower are more damaged. Moreover, a possible critical scenario due to increasing tower tilt is investigated. Finally, a viscoelastic formulation of the materials at the base of the tower is used to account for creep to understand the possible viscous effects at the base of the tower. Full article

A novel method for recognizing acoustic signals of partial discharge optical fiber sensors using the time-frequency phase composition property is proposed in this paper. The method involves obtaining the Wigner–Ville time-frequency distribution for acoustic signals from partial discharge optical fiber sensors through the Cohen bilinear time-frequency transformation, which provides a high time-frequency resolution. The Wigner–Ville distribution could reflect the insulation defect-related properties in detail, owing to the fact that the intensity distribution in the time domain and energy distribution in the frequency domain is seriously influenced by medium dispersion and acoustic propagation. The time-frequency phase composition property is implemented by combining the Wigner–Ville distributions at different phases in the power cycle, which comprehensively represent the characteristics of the acoustic signals from partial discharge optical fiber sensors. A Vision Transformer with an attention block is introduced to identify the acoustic signals of partial discharge sensors. The attention block ensures that the neural network assigns more weight to the energy concentration areas in the extracted acoustic features. To validate the proposed approach, experiments are conducted to identify the acoustic signals of partial discharge optical fiber sensors. The proposed method achieves an impressive accuracy of 99.56% on three group testing sets. This indicates that the proposed approach is a promising method for identifying acoustic signals of partial discharge sensors to detect various insulation defects using acoustic emission feature analysis. Full article

Accurate damage characterization in thermoset Carbon Fiber-Reinforced Polymer (CFRP) composites using Acoustic Emission (AE) requires statistically robust and interpretable models. This study employs multinomial logistic regression with forward selection and Type III analysis to identify the minimal set of AE parameters necessary for classifying damage mechanisms (fiber breaks, delamination, matrix cracks) in quasi-isotropic thermoset CFRP laminates under synchronously recorded load conditions. Starting from 18 conventional time- and frequency-domain descriptors, forward selection yielded seven candidate predictors. However, Type III analysis revealed that only four parameters, Load, Initiation Frequency, Amplitude, and Average Frequency, provide unique, statistically significant contributions (p < 0.05). The remaining predictors became redundant once these four were included. Machine learning and deep learning models trained on this minimal feature set achieved validation accuracies up to 98.7% on external specimens. High-frequency components (>1 MHz), as recorded at the sensor location after propagation and sensor convolution, were associated with fiber break events at elevated loads, while delamination events exhibited higher amplitude and lower-frequency content (<200 kHz) compared to matrix crack events. These observed frequency ranges reflect the combined effects of source mechanisms, guided wave dispersion in the 2.4 mm thick laminate, PWAS sensor response, and HDT-based hit segmentation, and are consistent with established AE damage signatures in literature. The results indicate that this four-parameter set is sufficient to classify the labeled AE waveform classes under monotonic tensile loading of quasi-isotropic [45/90/−45/0]_2s laminates, achieving 98.7% agreement with reference labels assigned via waveform morphology and spectral analysis. The proposed approach reduces computational overhead and enhances interpretability for structural health monitoring applications, pending validation across broader material systems and loading scenarios. A limitation of this study is that reference labels were assigned using waveform morphology and spectral analysis, lacking independent physical validation (e.g., microscopy). Full article

To address the demand for underwater acoustic detection with hydrostatic pressure resistance, this paper proposes a fiber-optic Fabry–Perot (F-P) underwater acoustic sensor based on micro-electromechanical system (MEMS) technology. According to the F-P interference principle, the diaphragm deforms under acoustic pressure, inducing variations in the F-P cavity length which modulate the interference spectrum and enable the measurement of underwater acoustic signals. A sensing diaphragm with a composite structure consisting of a central boss and a micro-hole array is designed, which improves the optical signal quality while reducing the influence of the pressure difference between the inner and outer surfaces of the diaphragm on sensor operation. MEMS fabrication, computer numerical control (CNC) machining, and laser fusion splicing technologies are employed to achieve batch fabrication of the sensing units and adhesive-free integration of the sensor. Experimental results show that the proposed sensor exhibits a flat frequency response within ±1.5 dB over the range of 1 kHz to 10 kHz, with an average signal-to-noise ratio (SNR) of 86.35 dB. The sensitivity reaches −181.79 dB re 1 rad/μPa at 10 kHz, with a maximum nonlinearity of 0.48% F.S., a repeatability error of 0.15% F.S. and a dynamic range of 100.83 dB. The proposed sensor features miniaturization, high consistency, hydrostatic pressure self-balancing capability, and immunity to electromagnetic interference, providing a solid foundation for hydrostatic-pressure-resistant underwater acoustic measurements in deep-sea environments. Full article

The purpose of this paper is to investigate the seismic damage performance levels of reinforced concrete (RC) external beam–column joints exhibiting beam flexural failure mode based on acoustic emission (AE) characteristics. To achieve this purpose, two specimens of RC external beam–column joints with beam flexural failure mode were tested under constant axial compression at the column and low-cyclic lateral loading at the end of the beam. During the tests, six AE-based indicators—namely AE hit ($H_{\mathrm{AE}}$), AE energy ($E_{\mathrm{AE}}$), AE count ($C_{\mathrm{AE}}$), amplitude ($A_{\mathrm{AE}}$), rise time (RT), and peak frequency ($f_{\mathrm{p}}$)—were measured using the PCI-2 Acoustic Emission System equipped with R6$\alpha$ piezoelectric sensors. In addition, five damage performance levels, i.e., no damage, minor damage, medium damage, serious damage, and collapse, were proposed based on the analysis of AE monitoring results. After calibration, the fiber finite element method was used to conduct a numerical simulation of 432 joints subjected to lateral loading. An empirical expression for the material parameter of the Park–Ang damage model was presented based on simulated results. Suggested five damage performance levels were used together with a response databank from the numerical analysis to obtain the limit damage values. This work provides a quantitative AE-based framework for seismic damage assessment of RC external beam–column joints with beam flexural failure mode, which can inform performance-based seismic design and post-earthquake safety evaluation. Full article

Partial-discharge (PD) monitoring is essential for assessing the insulation condition of generator stator bars. Conventional methods are susceptible to electromagnetic interference and are difficult to deploy in confined stator geometries. Fiber-optic acoustic detection technology offers strong immunity to electromagnetic interference and is suitable for the narrow and high-interference environment of stator bars, but it cannot directly provide discharge magnitude information. Therefore, in this study, fiber-optic acoustic detection technology was employed to acquire partial discharge acoustic signals from stator bars, and a mandrel-type fiber-optic acoustic sensor was developed, with PD tests performed on full-scale stator bars with internal defects. Meanwhile, considering the complex temporal characteristics of PD acoustic signals, a hybrid neural network—Transformer–convolutional neural network–long short-term memory (Transformer–CNN–LSTM)—was constructed for long-term time-series modeling to establish the mapping between acoustic signals and discharge magnitude intervals. The results indicate that fiber-optic acoustic detection enables sensitive and stable detection of weak PD acoustic signals. Phase-resolved PD (PRPD) patterns from the proposed system align with the discharge characteristics of internal defects, with the acoustic signal showing a phase lag relative to the electrical PD signal. The hybrid model achieved an overall interval estimation accuracy of 96.6%, outperforming CNN and CNN-LSTM models, with accuracies of 100% and 99.4% for discharge magnitude intervals below 100 pC and above 2000 pC, respectively. Full article

Long-distance coal gangue slurry transportation pipelines are critical components of underground coal mine green backfilling systems, yet blockage failures severely threaten their safe and efficient operation. Existing distributed acoustic sensing (DAS)-based monitoring methods for such pipelines suffer from three key limitations: insufficient fixed-point quantitative accuracy, lack of verified blockage-specific characteristic indicators, and limited quantitative severity assessment capability. To address these gaps, this paper proposes a novel feature-level fusion monitoring method integrating DAS, fiber Bragg grating (FBG), and piezoelectric accelerometers for accurate blockage identification and quantitative evaluation in coal gangue slurry pipelines. A slurry pipeline circulation test platform with gradient blockage simulation (0% to 76.42%) and a synchronous multi-sensor monitoring system were developed. Through multi-domain signal analysis, three blockage-correlated characteristic frequencies were identified and cross-validated by synchronous multi-sensor data: 1.5 Hz (system background vibration), 26 Hz (blockage-induced fluid–structure resonance, verified by the Euler–Bernoulli beam theory with a theoretical value of 25.7 Hz), and 174 Hz (transient flow impact). The DAS phase change rate exhibited a unimodal nonlinear response to blockage degree, with the peak occurring at 40.94% blockage. On this basis, a sine-fitting quantitative inversion model was developed, achieving a high goodness of fit (R² = 0.985), and leave-one-out cross-validation confirmed its excellent robustness with a mean relative prediction error of 3.77%. Finally, a collaborative monitoring framework was built to fully leverage the complementary advantages of each sensor, realizing full-process blockage monitoring covering global blockage localization, precise quantitative severity calibration, and high-frequency transient risk early warning. The proposed method provides a robust experimental and technical foundation for real-time early warning, precise localization, and quantitative diagnosis of long-distance slurry pipeline blockages and holds important engineering application value for the safe and efficient operation of underground coal mine green backfilling systems. Full article