Search Results (20,964)

Deep learning (DL)-based percussion methods in concrete-filled steel-tube (CFST) void detection have gained much attention. However, the detection signal contains a large amount of noise, which affects the accuracy of qualitative and quantitative analyses of the subsequent detection results. To improve the signal-to-noise ratio (SNR) during percussion detection, this study proposes a CFST void detection method using the good point set and vibrational snow ablation optimizer (GVSAO) algorithm and dual-channel parallel convolutional neural networks (CNNs). The proposed method employs the gram angle field (GAF) to transform percussive sound signals into images. It then constructs a dual-channel parallel CNN structure, where the GAF is decomposed into the following two maps: the gram angle sum field (GASF) and the gram angle difference field (GADF). These maps are simultaneously fed into the CNN for training. The outputs from the two channels are concatenated and fused. Finally, the GVSAO algorithm was used for model optimization to improve convergence speed and recognition accuracy. Both the temporal and spatial characteristics of the knocking sound signal are fully preserved, while the interference of different construction noises is effectively avoided. Validation experiments were conducted on CFST specimens with different heights of voids (0, 50, 100, and 150 mm) under different pressure loads. The original sample dataset and the signal-enhanced dataset were obtained by adding background noise with different SNRs. The test results show that the prediction accuracies on the original signal dataset are consistently above 98.74%. Among them, the accuracy achieves 100% at pressure loads of 0 and 50 tons. Additionally, the prediction accuracies on the signal-enhanced dataset are all above 97.2%, indicating that the model maintains a high level of classification performance. This suggests that the model can effectively suppress noise and exhibits excellent robustness. Full article

The distributed electric propulsion (DEP) system offers significant potential for enhancing aerodynamic efficiency, reducing emissions, and enabling innovative aerodynamic configurations. However, the strong coupling between propeller slipstream effects and wing structural dynamics presents new challenges for aeroelastic analysis. To address this issue, this paper proposes an aeroelastic modeling approach tailored for DEP systems that systematically accounts for the effects induced by propeller slipstreams. Specifically, the induced velocity generated by the propeller slipstreams is computed using a slipstream tube model and incorporated into the unsteady aerodynamic modeling via the unsteady vortex lattice method. Under appropriate assumptions, a state-space formulation of the unsteady aerodynamic forces is derived, while the wing structural dynamics are represented using the finite element method. After establishing the subsystem models, a complete aeroelastic model of the DEP system is assembled based on the input–output relationships among the subsystems. Nonlinear simulations are conducted using this integrated model. The results demonstrate the potential of distributed propellers for suppressing wing vibrations and alleviating structural loads. Full article

Detecting rare structural damage without labeled fault data remains a critical unsolved challenge in structural health monitoring (SHM). Prestressed concrete catenary poles are key elements of high-speed railway infrastructure, and undetected degradation can compromise safety and service reliability. This paper introduces BcDCGAN, a Bayesian conditional deep convolutional generative adversarial network designed for unsupervised anomaly detection in multivariate vibration time series from three in-service catenary poles. Trained exclusively on healthy acceleration signals with wind-speed conditioning, the model learns the normal structural dynamics and produces an uncertainty-based anomaly score that combines reconstruction quality, adversarial evaluation, and epistemic uncertainty into a single decision function. An adaptive, data-driven threshold estimate from healthy validation data enables practical deployment without damage labels. On a real 2017 catenary pole dataset (1606 signals, 70/10/20 split) with injected, physically motivated damage-like patterns, BcDCGAN achieves high anomaly recall with interpretable uncertainty signals and clear separation between normal and anomalous latent representations. The results suggest that Bayesian conditional GANs can support risk-aware monitoring of railway infrastructure under varying environmental and operational conditions. Full article

Vibration-based fault diagnosis is widely used for rotating machinery health monitoring, but practical diagnosis is often limited by scarce fault labels and uncertain measurement length. Longer vibration records can improve decision reliability but increase sensing and computational cost, whereas overly short records may yield unreliable predictions under noise and measurement corruptions. This paper studies few-shot fault diagnosis as a measurement-constrained decision task, in which the model identifies the fault class and determines when sufficient vibration evidence has been acquired. We propose a measurement-efficient diagnosis framework that combines prior knowledge from unlabeled healthy signals, physically constrained augmentation of scarce labeled samples, and adaptive early stopping in a shared one-dimensional feature extractor. The framework is evaluated on the UORED-VAFCLS and Paderborn University bearing datasets under 6-, 8-, and 10-shot settings with controlled corruption levels. Results show robust diagnostic performance with fewer acquired vibration windows than with fixed-length inference. In the representative PU-Hard 8-shot setting, the proposed method achieves 80.26% accuracy with an average of 1.2432 acquired windows and reduces the evaluation cost J from 0.3929 to 0.2596 compared with fixed four-window inference. These results indicate that adaptive measurement improves the accuracy–cost trade-off in few-shot vibration diagnosis. Full article

This study presents the analysis of global dynamics and stability (e.g., coexisting attractors, Hopf bifurcation boundary) for a nonlinear rotor system with an automatic ball balancer (ABB). The presence of nonlinearity, anisotropy and non-ideal dynamics makes this system not fully understood. The Lagrangian is written explicitly in terms of the displacement of the rotor centre and the angular positions of the balls $(x,y,\psi ,{\phi }_j)$. The kinetic energy separates into structural, unbalance coupling, and ball coupling blocks, and the Rayleigh dissipation function covers both support damping and race drag. The three families of equations of motion (translational, spin, ball) are compacted into the matrix form and solved numerically. Non-dimensionalisation introduces the seven groups ($\Omega$, ${\mu }_{\mathrm{un}},{\mu }_b,\epsilon ,\widehat{\beta },\widehat{D},$$\Delta$) with $\Delta$ being the anisotropy parameter. The results document bistability between the clustered and balanced ball configurations depending solely on ball initial conditions rather than rotor displacement, together with a basin of attraction analysis in which the balanced basin occupies only approximately $20\%$ of ball initial-condition space. A three-dimensional stability map reveals a previously unreported phenomenon: narrow islands of stability at very low race damping, suggesting that effective balancing may not always require dissipation, alongside a two-lobe Hopf bifurcation boundary with a disconnected instability pocket. Anisotropy study uncovers that the rotor’s response is dominated by quasi-periodic torus attractor across almost the entire ($93.5\%$) parameter space rather than the simple periodic balancing usually assumed, with a clean analytical rule identifying exactly when support asymmetry will resonantly amplify vibration. Together these findings point to design principles on ball seeding, damping selection, and permissible anisotropy. Full article

Space gravitational wave detection missions demand ultra-stable calibration of inertial sensor capacitive sensing. Conventional dynamic methods suffer from mechanical vibration noise and bias separation difficulties, while large-displacement operation introduces pronounced nonlinearity. This work proposes a static calibration method using an image-based displacement measurement system to establish a vibration-free benchmark. A subpixel edge detection algorithm locates the Test Mass and Electrode Housing edges with a repeatability of approximately 0.05 pixels, and the Test Mass geometry is independently calibrated by a Coordinate Measuring Machine (CMM, ±2 µm, $k=2$) to provide SI traceability. A nonlinear calibration model incorporating higher-order Taylor terms is developed, combined with a forward/reverse connection technique for composite bias modeling. Experimental validation at $x_0=665$ µm ($x_0/d_0\approx 0.665$) demonstrated a gain coefficient repeatability of 0.01658% RMSPER and a combined expanded uncertainty of $U\approx 2.18\times {10}^{-5}$$1/\text{µ}\mathrm{m}$ ($k=2$). Intended as a complementary ground-based technique to dynamic calibration, this method avoids dynamic excitation-induced noise while establishing complete SI traceability, offering a reliable solution for ground validation and long-term monitoring of space inertial sensors. Full article

Accurate identification of axial forces in beam structures with unknown boundary conditions is important for structural assessment and safety monitoring. Most existing methods are based on Euler–Bernoulli beam theory and neglect the effect of rotational inertia. This simplification may reduce the accuracy of axial force identification for short beam members. To address this limitation, this study develops an axial force identification method that accounts for rotational inertia effects. First, a free-vibration governing equation for axially loaded beam members is derived based on the Reissner energy approach. Compared with the Euler–Bernoulli beam, the derived equation further accounts for the effect of rotational inertia. Then, based on the proposed dynamic formulation, an axial force identification method applicable to beam members with unknown boundary conditions is established by utilizing measured natural frequencies and mode shapes. Finally, the effectiveness and accuracy of the proposed method are systematically validated through both numerical simulations and experimental investigations on beam members. Numerical results indicate that incorporating rotational inertia improves axial force identification accuracy compared with conventional approaches, particularly for short beam members and higher-order modes. Experimental results further confirm its effectiveness, with a maximum identification error reduction of 7.69%. Full article

Efficient bridge scanning and documentation are crucial for creating reliable digital 3D models. However, scanning workflows often rely on implicit practitioner experience rather than standardized protocols. This paper presents practical insights derived from a Multiple Case Study (MCS) of ten heterogeneous, real-world bridges in Germany. The study evaluates Terrestrial Laser Scanning (TLS), Mobile Laser Scanning (MLS) and Unmanned Aerial Systems (UAS) photogrammetry. The findings isolate distinct performance trade-offs. TLS offers high accuracy but suffers from shadowing occlusions. Conversely, UAS provides operational flexibility but introduces geometric vulnerabilities, including photogrammetric reconstruction noise on fine structures and SLAM trajectory drift on vibrating spans. To unify these insights, a generalized, BPMN-compliant process model mapping the complete data acquisition lifecycle under legal and spatial constraints is defined. This research provides an actionable, practical guide to optimize data quality and efficiency in structural engineering workflows. Full article

Lycopene is a highly significant carotenoid in daily life, exhibiting potent antioxidant properties and recognized as one of the most powerful natural antioxidants identified in plants to date. Its functionality originates from electronic and vibrational states that exhibit a high sensitivity to environmental perturbations. Nevertheless, exclusively experimental methodologies face challenges in delivering a comprehensive molecular-level comprehension of the influence exerted by particular environmental factors on the vibronic characteristics. This deficiency in understanding hinders the accurate prediction of its behavior and functional performance within complex systems. The first principle computational investigation enables a precise elucidation of the coupling mechanisms between electronic excitations and vibrational modes under diverse solvation and interaction environments. The results indicate that the local environment significantly influences the charge distribution and orbital energies of lycopene, altering its vibrational and electronic state properties. This provides a fundamental theoretical framework for predicting their photophysical behavior and biological functions within complex matrices. Full article

This paper proposes a vibration-based approach for real-time condition monitoring of Friction Stir Welding (FSW) tools, which are widely used in the marine and automotive industries. Conventional inspection techniques such as visual examination and endoscopy are not practicable during active welding operations. The Locally Weighted Learning (LWL) algorithm, a lazy learning method, is used to address this limitation. Vibration signals are collected from a PLC-controlled FSW machine under five tool conditions, statistical features are extracted from the raw data, and a J48 decision tree is applied for feature selection to reduce computational overhead. Classification performance is evaluated using three lazy learning algorithms K-star (K*), LWL, and k-Nearest Neighbour (kNN) with LWL yielding the best result. The previously reported best accuracy for the same FSW setup was 73.16% at 1400 rpm using Random Forest; the proposed LWL-based approach achieves 92% accuracy under identical conditions, enabling earlier detection of tool faults before they result in weld defects or component failures. Full article

Detecting and localizing leaks in buried pipelines typically requires direct access to the pipe, which is often impractical in real-world conditions. Although ground-surface vibration measurements offer a non-intrusive alternative, their potential for spatial leak localization remains underexplored, particularly in relation to frequency-dependent attenuation effects. This study investigates how frequency-dependent energy decay encodes spatial information in leak-induced ground vibrations. Experimental wok was conducted using an outdoor buried pipeline testbed, where surface acceleration data were collected with a movable array of piezoelectric sensors. The measurements were reorganized into L-shaped sensor trios to enable directional analysis and increase the number of spatial configurations. Energy-based features extracted from discrete frequency bands were used to represent the leak signatures, capturing both attenuation behavior and soil–pipe interaction effects. Artificial Neural Network and Random Forest models were trained to estimate leak coordinates in a local reference frame. The results demonstrate high localization accuracy at the centimeter scale and reveal consistent relationships between prediction error, distance, and signal-to-noise ratio. These findings show that frequency-dependent attenuation provides a robust basis for spatial inference, and that combining ground surface vibration measurements with lightweight machine learning models offers an effective and non-intrusive solution for leak localization in buried pipelines. Full article

With the rapid development of intelligent manufacturing, edge computing, and industrial and financial–industrial digital systems, large volumes of multisource hardware sensing signals are continuously generated in complex production environments, including environmental, electrical, vibration, network communication, and device operational signals. Owing to the heterogeneity, asynchrony, noise interference, and disturbance sensitivity of these signals, conventional state prediction methods often fail to sufficiently characterize the dynamic response relationships among different sensing sources and cannot maintain stable prediction performance under non-stationary scenarios such as load surges, network congestion, and device anomalies. To address these challenges, a multisource hardware sensing signal fusion network is proposed for the edge-computing and digital production test scenario of an intelligent equipment manufacturing enterprise in Hebei Province, China, with the aim of achieving robust state prediction and anomaly perception in complex digital systems. In the proposed method, environmental sensing, device power, edge-node operation, vibration monitoring, network communication, and system output states are uniformly modeled as multisource engineering sensing signals, and an end-to-end prediction framework is constructed with cross-source sensing signal alignment to facilitate temporal coherence, disturbance-aware residual correction to substantially mitigate disturbance contamination, and context-adaptive fusion. Experimental results show that the proposed method achieves the best performance in the overall state prediction task, with MAE, RMSE, MAPE, and $R^2$ reaching $0.0968$, $0.1457$, $8.12$%, and $0.9416$, respectively, outperforming baseline methods including ARIMA, XGBoost, LightGBM, LSTM, TCN, Transformer, Attention Fusion, and Multimodal Transformer. In the disturbance robustness experiment, the Event-MAE and Event-RMSE of the proposed method are reduced to $0.1126$ and $0.1694$, respectively, with an Avg. Drop of only $28.98$%, indicating that more stable responses can be achieved under non-stationary disturbance scenarios. In the abnormal-state recognition task, Accuracy, Precision, Recall, and F1-score values of $94.32$%, $93.76$%, $92.85$%, and $93.30$% are achieved, respectively. The results demonstrate that the proposed method can effectively improve the state prediction accuracy, disturbance robustness, and anomaly warning capability of multisource hardware sensing data in complex industrial and financial–industrial digital systems, thereby providing an effective modeling scheme for intelligent monitoring and engineering decision-making in AI-driven industrial and financial sensing scenarios. Full article

Severe longitudinal vibrations and abnormal tension fluctuations in hoisting ropes pose significant threats to the safe and stable operation of mine hoisting systems. To address these issues, this paper proposes a semi-active vibration-suppression strategy combining a magnetorheological damper (MRD) with output-feedback adaptive sliding-mode control (ASMC). A dynamic model of the MRD-equipped hoisting system is developed using Hamilton’s principle. The nonlinear hysteresis of the MRD is described by a simplified extended hyperbolic tangent function model (SEHTFM), and an inverse model converts the desired control force into a feasible real-time current command. Using only displacement and velocity measurements at the conveyance–rope connection, the ASMC compensates for matched uncertainties, including boundary excitation, modeling and truncation errors, and force-realization errors. Numerical simulations compare an optimized passive viscous damper benchmark, SMC–MRD, and ASMC–MRD responses under varying payloads, accelerations, and hoisting speeds. During constant-speed operation, ASMC–MRD achieves peak reduction rates of 82.8% in dynamic displacement and 77.6% in dynamic tension relative to the optimized passive benchmark. The results demonstrate accurate force realization with small bounded tracking errors and improved robustness under variable operating conditions. Full article

Dye removal from aqueous solutions remains a major global environmental challenge. Among the various remediation techniques, adsorption using natural materials has gained significant attention. In this study, the adsorption of methylene blue (MB) by a natural volcanic rock (VR) adsorbent—collected from the Cerro Blanco volcano in Divisaderos, Sonora, Mexico—was investigated, and the process efficiency was evaluated at different temperatures. The comprehensive characterization revealed a rough and irregular porous surface via SEM, while the EDS elemental data and the CIPW normative calculations identified the material as a silica-saturated tholeiitic basalt, primarily composed of bytownite (${An}_{71})$ and pyroxenes. This petrological classification was cross-validated by XRD and FTIR spectra, which exhibited vibrational modes characteristic of mafic silicate. The surface analysis via the BET method indicated a specific surface area of 12 m²·g⁻¹, while a BJH analysis indicated a mesoporous structure (average pore diameter of 3.75 nm), and a Type IV isotherm with H3-type hysteresis, suggesting narrow, slit-shaped pores. Batch adsorption experiments demonstrated an exceptional removal efficiency of 99.99% for 50 mg·L⁻¹ MB within only 30 min. The equilibrium data and the adsorption kinetics followed the Langmuir isotherm and a pseudo-second-order model, respectively. Cytotoxicity assays confirmed the VR is biosafe. The combination of high removal efficiency, low cost, and environmental safety positions this material as high-potential adsorbent for sustainable water remediation processes. Full article

Polysaccharides-based bioactive films are regarded as a promising strategy to delay fruit spoilage. β-cyclodextrins (CD) was incorporated into pullulan/Astragalus extract (AE) bioactive films (CD-AE-P) to systematically investigate its effect on multi-scale structures features, physicochemical properties, migration behavior, anti-browning efficacy, and aroma substances in sliced apples. With increasing CD content, a slight blue shift was observed in O-H stretching vibration peak; CD-AE-P films retained an amorphous structure, as evidenced by the disappearance of scattering peak at 0.075 < q < 0.25 nm⁻¹, and they exhibited smoother, continuous fractured surfaces with sparsely distributed protrusions. Meanwhile, CD incorporation contributed to the preservation of macromolecular thermal stability and induced an initial increase followed by a decrease in storage modulus (E′), with CD15-AE-P sample exhibiting the highest Tg value. Although the hydrophilicity increased marginally in the films upon adding CD, WVP decreased progressively, reaching a 23.3% reduction in CD25-AE-P relative to CD0-AE-P. Furthermore, total migration from the films was significantly suppressed by CD incorporation, and the anti-browning effect on sliced apples was extended; notably, treatment with CD25-AE-P effectively preserved the aroma substances of sliced apples during storage. These results demonstrate that CD serves as an effective functional carrier to modulate the effective release of bioactive compounds in active food packaging systems. Full article