Search Results (1,032)

Despite advances in Convolutional Neural Networks (CNNs) for intelligent fault diagnosis in CNC machine tools, bearing fault diagnosis in CNC feed systems remains challenging, particularly in multi-scale feature extraction and generalization across operating conditions. This study introduces an enhanced multi-scale feature network (MSFN) that addresses these limitations through three integrated modules designed to extract critical fault features from vibration signals. First, a Soft-Scale Denoising (S2D) module forms the backbone of the MSFN, capturing multi-scale fault features from input signals. Second, a Multi-Scale Adaptive Feature Enhancement (MS-AFE) module based on long-range weighting mechanisms is developed to enhance the extraction of periodic fault features. Third, a Dynamic Sequence–Channel Attention (DSCA) module is incorporated to improve feature representation across channel and sequence dimensions. Experimental results on two datasets demonstrate that the proposed MSFN achieves high diagnostic accuracy and exhibits robust generalization across diverse operating conditions. Moreover, ablation studies validate the effectiveness and contributions of each module. Full article

Complex mechanical systems, such as agricultural combine harvesters, are subjected to dynamic excitations from multiple coupled sources, compromising structural integrity and operational reliability. Disentangling these vibrations to identify dominant sources and quantify their transmission paths remains a significant engineering challenge. This study proposes a robust diagnostic framework to address this issue. We employed a multi-condition vibration test with sequential source activation and an improved Operational Transfer Path Analysis (OTPA) method. Applied to a harvester chassis, the results revealed that vibration energy is predominantly concentrated in the 0–200 Hz frequency band. Path contribution analysis quantified that the “cutting header → conveyor trough → hydraulic cylinder → chassis frame” path is the most critical contributor to vertical vibration, with a vibration acceleration level of 117.6 dB. Further analysis identified the engine (29.3 Hz) as the primary source for vertical vibration, while lateral vibration was mainly attributed to a coupled resonance between the threshing cylinder (58 Hz) and the engine’s second-order harmonic. This study’s theoretical contribution lies in validating a powerful methodology for vibration source apportionment in complex systems. Practically, the findings provide direct, actionable insights for targeted structural optimization and vibration suppression. Full article

The aim of this paper is to tailor the geometry and material parameters of a three-layer railway track model to achieve favorable properties for the circulation of high-speed trains at very high velocities. The three layers imply that the model should have three critical velocities for resonance. However, in many cases, some of these values are missing and must be replaced by pseudo-critical values. Since no resonance occurs at pseudo-critical velocities, even in the absence of damping, deflections never reach infinity. By using optimization techniques, it is possible to adjust the model’s parameters, so that the increase in vibrations remains minimal and does not pose a real danger. In this way, circulation velocities could be extended beyond the critical value, thereby increasing the network capacity and, consequently, improving the competitiveness of rail transport compared to other modes of transportation, thus contributing to decarbonization. The presented results are preliminary and require further analysis and validation. Several optimization techniques are implemented, leading to the establishment of designs that already have rather high pseudo-critical velocities. Further research will show how these theoretical findings can be utilized in practice. Full article

On 22 May 2012, a magnitude Mw 5.6 earthquake struck the Pernik region of western Bulgaria, causing structural damage in nearby cities, including Sofia. This study assesses the seismic response of a six-story reinforced concrete building in central Sofia, utilizing real accelerogram data recorded at the basement (SGL1) and sixth floor (SGL2) levels during the earthquake. Using the Kanai–Yoshizawa (KY) model, the study estimates inter-story motion and assesses amplification effects across the structure. Analysis of peak ground acceleration (PGA), velocity (PGV), displacement (PGD), and spectral ratios reveals significant dynamic amplification of peak ground acceleration and displacement on the sixth floor, indicating flexible and dynamic behavior, as well as potential resonance effects. The analysis combines three spectral techniques—Horizontal-to-Vertical Spectral Ratio (H/V), Floor Spectral Ratio (FSR), and the Random Decrement Method (RDM)—to determine the building’s dynamic characteristics, including natural frequency and damping ratio. The results indicate a dominant vibration frequency of approximately 2.2 Hz and damping ratios ranging from 3.6% to 6.5%, which is consistent with the typical damping ratios of mid-rise concrete buildings. The findings underscore the significance of soil–structure interaction (SSI), particularly in sedimentary basins like the Sofia Graben, where localized geological effects influence seismic amplification. By integrating accelerometric data with advanced spectral techniques, this research can enhance ongoing site-specific monitoring and seismic design practices, contributing to the refinement of earthquake engineering methodologies for mitigating seismic risk in earthquake-prone urban areas. Full article

In this study, we report the development of a novel magnetized coal fly ash-supported nano-silver composite (AgNPs/MCFA) for dual-functional applications in wastewater treatment: the efficient degradation of methyl orange (MO) dye and broad-spectrum antibacterial activity. The composite was synthesized via a facile impregnation–reduction–sintering route, utilizing sodium citrate as both a reducing and stabilizing agent. The AgNPs/MCFA composite was systematically characterized through multiple analytical techniques, including Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and vibrating sample magnetometry (VSM). The results confirmed the uniform dispersion of AgNPs (average size: 13.97 nm) on the MCFA matrix, where the formation of chemical bonds (Ag-O-Si) contributed to the enhanced stability of the material. Under optimized conditions (0.5 g·L⁻¹ AgNO₃, 250 °C sintering temperature, and 2 h sintering time), AgNPs/MCFA exhibited an exceptional catalytic performance, achieving 99.89% MO degradation within 15 min (pseudo-first-order rate constant k_a = 0.3133 min⁻¹) in the presence of NaBH₄. The composite also demonstrated potent antibacterial efficacy against Escherichia coli (MIC = 0.5 mg·mL⁻¹) and Staphylococcus aureus (MIC = 2 mg·mL⁻¹), attributed to membrane disruption, intracellular content leakage, and reactive oxygen species generation. Remarkably, AgNPs/MCFA retained >90% catalytic and antibacterial efficiency after five reuse cycles, enabled by its magnetic recoverability. By repurposing industrial waste (coal fly ash) as a low-cost carrier, this work provides a sustainable strategy to mitigate nanoparticle aggregation and environmental risks while enhancing multifunctional performance in water remediation. Full article

Coatings that are tolerant of poor surface preparation are often used for rapid, real-time maintenance of aging steel surfaces. In this study, a modified epoxy (EP) anti-rust coating was proposed, utilizing methyl gallate (MG) as a rust conversion agent, graphene oxide (GO) as an active functional material, and epoxy resin as the film-forming material. The anti-rust mechanism was investigated using potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM), laser scanning confocal microscopy (LSCM), and the scanning vibration electrode technique (SVET). The results demonstrated that over a period of 21 days, the impedance of the coating increases while the corrosion current density decreases with prolonged soaking time. The coating exhibited a maximum impedance of 2259 kΩ, and a lower corrosion current density of 8.316 × 10⁻³ A/m², which demonstrated a three-order magnitude reduction compared to the corrosion current density observed in mild steel without coating. LSCM demonstrated that MG can not only penetrate the tiny gap between the rust particles, but also effectively convert harmful rust into a complex. SVET showed a much more uniform current density distribution in the micro-zones of mild steel with the anti-rust coating compared to uncoated mild steel, indicating that the presence of GO not only enhanced the electrical conductivity of the coating, but also improved the structure of the coating, which contributed to the high performance of the modified epoxy anti-rust coating. This work highlights the potential application of anti-rust coating in the protection of metal structures in coastal engineering. Full article

Roller bearing manufacturing errors have been proven to be critical factors affecting the vibrations of gear systems. Waviness is one main form of manufacturing error affecting the operational performance and life of bearings. However, most previous studies did not completely incorporate the effects of the uneven bearing waviness on the flexible gear system vibrations. To characterize the contribution of the uneven bearing waviness on the vibrations of the gear system, a gear transmission system dynamics model considering shaft flexibility was established. The evenness sinusoidal waviness model (SWM) and uneven sinusoidal waviness model considering the time-varying contact (SWMS) were compared. The influences of the time-varying gear meshing stiffness excitations and flexibilities of shafts on the vibrations of the gear system were considered. A dynamic model was established, and the vibrations of the flexible gear system with the SWM and SWMS were compared. The vibrations induced by different amplitudes and orders of bearing waviness were analyzed. Note that the waviness of the bearing has a great influence on the system vibrations. The vibrations of the flexible gear system intensified with the increase in the bearing waviness order and amplitude. The vibrations from the gear system with the SWMS were bigger than those of the SWM. This paper introduces an alternative dynamic modeling model enabling the vibration analysis of the flexible gear system with evenness and uneven bearing waviness. Full article

Studying nonlinear oscillations in mechanical systems is fundamental to understanding complex dynamic behavior in engineering applications. While classical analytical methods remain valuable for systems with limited complexity, they become increasingly inadequate when nonlinearities are strong and geometrically induced, as in the case of large-amplitude oscillations. This paper presents a combined numerical and experimental investigation of a mechanical system composed of two coupled pendulums, exhibiting significant nonlinear behavior due to elastic deformation throughout their motion. A mathematical model of the system was developed using the MatLab/Simulink ver.6.1 environment, considering gravitational, inertial, and nonlinear elastic restoring forces. One of the major challenges in accurately modeling such systems is accurately representing damping, particularly in the absence of dedicated dampers. In this work, damping coefficients were experimentally identified through decrement measurements and incorporated into the simulation model to improve predictive accuracy. The simulation outputs, including angular displacements, velocities, accelerations, and phase trajectories over time, were validated against experimental results obtained via high-precision inertial sensors. The comparison shows a strong correlation between numerical and experimental data, with minimal relative errors in amplitude and frequency. This research represents the first stage of a broader study aimed at analyzing forced and parametrically excited oscillations. Beyond validating the model, the study contributes to the design of a robust experimental framework suitable for further exploration of nonlinear dynamics. The findings have practical implications for the development and control of mechanical systems subject to dynamic loads, with potential applications in automation, vibration analysis, and system diagnostics. Full article

Efficient maintenance is paramount for rail transport systems to avoid catastrophic accidents. Therefore, a method that enables the early detection of defects in critical components is crucial for increasing the availability of rolling stock and reducing maintenance costs. This work’s main contribution is the proposal of a methodology for analyzing vibration signals. The vibration signals, obtained from a bogie axle on a test bench, are decomposed into intrinsic functions, to which classical signal processing techniques are then applied. Finally, decision trees are employed to characterize the axle’s state, yielding excellent results. Full article

Worm gears are used in various mechanical constructions, especially in heavy industrial plants, where they are exposed to high operating loads, large torques, and high temperatures, particularly in conditions where it is necessary for the input and output shafts to be at an angle of 90°. Regarding tribological optimization, the application of carbon nanotube in lubricants can lead to significant improvements in the performance characteristics of worm gears, both in terms of increasing efficiency and reducing the coefficient of friction and wear, as well as minimizing mechanical losses, noise, and vibrations. The objective of this study is for the research results, through the use of oil with varying percentages of carbon nanotube additives (CNTs), to contribute to the optimization of worm gears by improving efficiency, extending service life, and reducing vibrations—both within the gearbox itself and within the industrial facility where it is applied. The research methodology involved laboratory testing of a worm gear using lubricants with varying concentrations of carbon nanotube. During the experiment, measurements of efficiency, vibrations, and noise levels were conducted in order to determine the impact of these additives on the operational performance of the gear system. The main contribution of this research is reflected in the experimental confirmation that the use of lubricants with optimized concentrations of carbon nanotube significantly enhances the operational performance of worm gears by increasing efficiency and reducing vibrations and noise, thereby enabling tribological optimization that contributes to improved reliability, extended service life, and enhanced workplace ergonomics under demanding industrial conditions. Furthermore, experimental investigations have shown that the efficiency of the gearbox increases from an initial value of 0.42–0.65, which represents an increase of 54%, the vibrations of the worm gear decrease from an initial value of 5.83–2.56 mm/s², which represents an decrease of 56%, while the noise was reduced from 87.5 to 77.2 dB, which represents an decrease of 12% with the increasing percentage of carbon nanotube additives in the lubricant, up to a maximum value of 1%. However, beyond this experimentally determined threshold, a decrease in the efficiency of the tested worm gearbox, as well as an increase in noise and vibration levels was recorded. Full article

Inelastic electron tunneling spectroscopy (IETS) has emerged as a powerful vibrational spectroscopy technique for molecular electronic junctions, providing unique insights into molecular vibrations and electron–phonon coupling at the nanoscale. In this review, we present a comprehensive overview of IETS in molecular junctions, tracing its development from foundational principles to the latest advances. We begin with the theoretical background, detailing the mechanisms by which inelastic tunneling processes generate vibrational fingerprints of molecules, and highlighting how IETS complements optical spectroscopies by accessing electrically driven vibrational excitations. We then discuss recent progress in experimental techniques and device architectures that have broadened the applicability of IETS. Central focus is given to emerging applications of IETS over the last decade: molecular sensing (identification of chemical bonds and conformational changes in junctions), thermoelectric energy conversion (probing vibrational contributions to molecular thermopower), molecular switches and functional devices (monitoring bias-driven molecular state changes via vibrational signatures), spintronic molecular junctions (detecting spin excitations and spin–vibration interplay), and advanced data analysis approaches such as machine learning for interpreting complex tunneling spectra. Finally, we discuss current challenges, including sensitivity at room temperature, spectral interpretation, and integration into practical devices. This review aims to serve as a thorough reference for researchers in physics, chemistry, and materials science, consolidating state-of-the-art understanding of IETS in molecular junctions and its growing role in molecular-scale device characterization. Full article

To address the challenge of fault diagnosis for in-wheel motors in four-wheel independent driving systems under variable driving conditions and harsh environments, this paper proposes a novel method based on two-stream 2DCNNs (two-dimensional convolutional neural networks) with a DCBA (depthwise convolution block attention) module. The main contributions are twofold: (1) A DCBA module is introduced to extract multi-scale features—including prominent, local, and average information—from grayscale images reconstructed from vibration signals across different domains; and (2) a two-stream network architecture is designed to learn complementary feature representations from time-domain and time–frequency-domain signals, which are fused through fully connected layers to improve diagnostic accuracy. Experimental results demonstrate that the proposed method achieves high recognition accuracy under various working speeds, loads, and road surfaces. Comparative studies with SENet, ECANet, CBAM, and single-stream 2DCNN models confirm its superior performance and robustness. The integration of DCBA with dual-domain feature learning effectively enhances fault feature extraction under complex operating conditions. Full article

Cellulose nanofiber (CNF) treatments can enhance the structure and performance of regenerated cellulose fibers. This study investigates the effects of CNF treatment on the mechanical properties, water absorption behavior, and humidity dependence of regenerated cellulose fibers. Tensile testing demonstrated that CNF-treated fibers exhibit improved elasticity and reduced swelling in aqueous environments. Scanning electron microscopy revealed the adsorption of CNF components onto the fiber surfaces. Microbeam X-ray diffraction indicated structural differences between untreated and CNF-treated fibers, with the latter containing cellulose I crystals. Small-angle X-ray scattering revealed alterations in the internal fibrillar structure due to CNF treatment. FT-IR spectroscopy highlighted humidity-dependent variations in molecular vibrations, with peak intensities increasing under higher humidity conditions. Additionally, CNF treatment inhibited water absorption in high-humidity conditions, contributing to reduced expansion rates and increased elastic modulus during water absorption. Overall, CNF treatment enhanced both the mechanical strength and water resistance of regenerated cellulose fibers, making them suitable for advanced textile applications. This study provides valuable insights into the role of CNF-treated fibers in improving the durability and functional performance of regenerated cellulose-based textile. Full article

This research experimentally and numerically evaluates the effectiveness of viscoelastic (VE) and rotational friction (RF) dampers in enhancing the seismic performance of coupled shear wall (CSW) systems. This study consists of two phases: (1) element testing to characterize the hysteretic behavior and energy dissipation capacity of VE and RF dampers, and (2) shake table testing of a large-scale CSW structure equipped with these dampers under the white noise, sinusoidal and Kokuji waves. The experimental results are validated through numerical analysis using STERA 3D (version 11.5), a nonlinear finite element software, to simulate the dynamic response of the damped CSW system. Key performance indicators, including inter-story drift, base shear, and energy dissipation, are compared between experimental and numerical results, demonstrating strong correlation. The findings reveal that VE dampers effectively control high-frequency vibrations, while RF dampers provide stable energy dissipation across varying displacement amplitudes. The validated numerical model facilitates the optimization of damper configurations for performance-based seismic design. This study provides valuable insights into the selection and implementation of supplemental damping systems for CSW structures, contributing to improved seismic resilience in buildings. Full article

A concentric double-flange butterfly valve (DN-500, PN-10) was analyzed to examine its dynamic behavior and internal fluid flow across multiple opening angles. Finite Element Analysis (FEA) was employed to determine natural frequencies, mode shapes, and effective mass participation factors (EMPFs) for valve positions at 30°, 60°, and 90°. The valve geometry was discretized using a curvature-based mesh with linear elastic isotropic properties for 1023 carbon steel. Lower-order vibration modes produced global deformations primarily along the valve disk, while higher-order modes showed localized displacement near the shaft–bearing interface, indicating coupled torsional and translational dynamics. The highest EMPF in the X-direction occurred at 1153.1 Hz with 0.2631 kg, while the Y-direction showed moderate contributions peaking at 0.1239 kg at 392.06 Hz. The Z-direction demonstrated lower influence, with a maximum EMPF of 0.1218 kg. Modes 3 and 4 were critical for potential resonance zones due to significant mass contributions and directional sensitivity. Computational Fluid Dynamics (CFD) simulation analyzed flow behavior, pressure drops, and turbulence under varying valve openings. At a lower opening angle, significant flow separation, recirculation zones, and high turbulence were observed. At 90°, the flow became more streamlined, resulting in a reduction in pressure losses and stabilizing velocity profiles. Full article