Search Results (398)

This study presents a probabilistic prediction method for train-induced vibrations by combining a deep neural network (DNN) with the mixture density model in a cascade fashion, referred to as the DNN-RMDN model in this paper. A benchmark example is conducted to demonstrate and evaluate the prediction performance of the DNN-RMDN model. Subsequently, the model is applied to a case study to investigate and compare the uncertainties of train-induced vibrations in the throat area and testing line area of a metro depot. After training, the model is capable of accurately predicting the probability density function (PDF) of train-induced vibrations at different distances from the track and at different frequencies. Utilizing the predicted PDF, probabilistic assessments can be performed to ascertain the likelihood of surpassing predefined limits. By employing a mixture density model instead of a single Gaussian distribution, the DNN-RMDN model achieves more accurate prediction of the PDF for train-induced vibrations. The proposed probabilistic assessment framework can effectively assist in vibration screening during the planning phase and in selecting and designing vibration mitigation measures of appropriate levels. Full article

This study addresses the persistent challenge of polysulfide dissolution in lithium–sulfur (Li–S) batteries by introducing magnesium oxide (MgO) nanoparticles as a novel additive. MgO was integrated with sulfur using a scalable process involving solid-state melt diffusion treatment followed by planetary ball milling. XRD measurements confirmed that sulfur (S₈) retains its orthorhombic crystalline structure (space group $F_{ddd}$) following the MgO incorporation, with minimal peak shifts indicating slight lattice distortion, while the increased peak intensity suggests enhanced crystallinity due to MgO acting as a nucleation site. Additionally, Raman spectroscopy demonstrated sulfur’s characteristic vibrational modes consistent with group theory (point group $D_{2}h$) and highlighted multiwalled carbon nanotube (MWCNT′s) D, G, and 2D bands, with a low I_D/I_G ratio (0.47), which indicated low defects and high crystallinity in the prepared cathode. The S–MgO composite cathode exhibited superior electrochemical behavior, with an initial discharge capacity (950 mA h g⁻¹ at 0.1 C), significantly improved compared to pristine sulfur’s. The presence of MgO effectively mitigated the polysulfide shuttle effect by trapping polysulfides, leading to enhanced stability over 400 cycles and the consistent coulombic efficiency of over 99.5%. After 400 cycles, EDS and SEM analyses confirmed the structural integrity of the electrode, with only minor fractures and slight sulfur content loss. Electrochemical impedance spectroscopy further confirmed the enhanced performance. Full article

The small rhizome Chinese herbal medicine harvesting machine is used for excavating the underground rhizomes of Chinese medicinal plants. The reliability of an existing machine of this type was found to be suboptimal due to high vibration levels, as confirmed by direct measurements. To remedy this issue, the differential equation of motion was derived, solved, and visualized using MATLAB software (R2017a). The impact of various parameters on the equation of motion was analyzed through both time and frequency domain plots, as well as experimental analysis. The parameters studied included the rotational speed of the tractor’s power take-off (PTO) shaft, the machine’s overall mass and stiffness, the transmission ratio, and the excitation force generated by the machine’s reciprocating parts. To reduce vibration in the non-resonant state and avoid resonance as the natural frequency changes, several modifications were necessary: the PTO speed needed to be controlled, the stiffness of the machine had to be increased, and the mass of the reciprocating parts had to be decreased. Additionally, expanding the transmission-ratio range of the operational machinery was essential. Sandbags were added to the machine’s frame to increase its overall mass. The above measures have reduced the vibration speed of the harvester during operation. The vibration speeds V_max and V_RMS of the harvester under both working and non-working conditions have decreased by half compared to their original values, reducing the occurrence of resonance in the harvester and effectively mitigating vibration damage, thereby enhancing operational reliability. Full article

The structural characteristics of large-span structures inherently differ from those of conventional multistorey structures, making it challenging to accurately describe the contribution of various vibration modes to the overall response using traditional dynamic response analysis methods. Based on the response spectrum method, this paper investigates the influence of the first torsional mode on the overall effects of large-span structures. It proposes a new metric, called the torsional mode contribution factor, to characterize the contribution of torsional modes. Focusing primarily on single-span frames, the study explores the impact of factors such as eccentricity ratio, aspect ratio, and roof stiffness on the torsional mode contribution factor. Additionally, the relationship between the period ratio and the torsional mode contribution factor is examined to assess the necessity of controlling the period ratio. The findings reveal that the contribution of torsional modes to the overall seismic response varies significantly under different conditions, such as eccentricity ratio, aspect ratio, roof stiffness, and torsional stiffness. The torsional mode’s contribution is minimal for small eccentricity ratios, with the response primarily driven by translational modes. As eccentricity increases, translational-torsional coupling becomes more pronounced, amplifying the influence of torsional modes on the overall dynamic response. The study also highlights that increasing roof stiffness and aspect ratios can mitigate torsional effects to a certain extent. Still, excessive eccentricity ratios and stiffness may result in higher torsional contributions. Additionally, it is found that increasing torsional stiffness reduces the influence of torsional modes but does not eliminate the overall torsional deformation. The proposed torsional mode contribution factor offers an effective way to quantify these effects, demonstrating that traditional control methods, such as period ratio control, may not fully capture the torsional contributions. Full article

The primary objective of modal identification for variable thickness quartz plates is to ascertain their dominant operating mode, which is essential for examining the vibration of beveled quartz resonators. These beveled resonators are plate structures with varying thicknesses. While the beveling process mitigates some spurious modes, it still presents challenges for modal identification. In this work, we introduce a modal identification technique based on the energy method. When a plate with variable thickness is in a resonant state of thickness–shear vibration, the proportions of strain energy and kinetic energy associated with the thickness–shear mode in the total energy reach their peak values. Near this frequency, their proportions are the highest, aiding in identifying the dominant mode. Our research was based on the Mindlin plate theory, and appropriate modal truncation were conducted by retaining three modes for the coupled vibration analysis. The governing equation of the coupled vibration was solved for eigenvalue problem, and the modal energy proportions were calculated based on the determined modal displacement and frequency. Finally, we computed the eigenvalue problems at different beveling time, as well as the modal energies associated with each mode. By calculating the energy proportions, we could clearly identify the dominant mode at each frequency. Our proposed method can effectively assist engineers in identifying vibration modes, facilitating the design and optimization of variable thickness quartz resonators for sensing applications. Full article

Predictive numerical models in the study of ground-borne vibrations generated by railway systems have traditionally relied on the subsystem partition approach (segmented). In such a method, loads are individually applied, and the cumulative effect of the rolling stock is obtained through superposition. While this method serves to mitigate computational costs, it may not fully capture the complex interactions involved in ground-borne vibrations—especially in the frequency domain. Recent advancements in computation and software have enabled the development of more sophisticated vibrational contamination prediction models that encompass the entire dynamics of the system, from the rolling stock to the terrain, allowing continuous simulations with a defined time step. Furthermore, the incorporation of computational contact mechanics tools between various elements not only ensures accuracy in the time domain but also extends the analysis into the frequency domain. In this novel approach, the segmented models are shifted to continuous simulations where the nonlinear problem of a rigid–flexible multibody system is fully considered. The model can predict the impact of a high-speed rail (HSR) vehicle passing, capturing the key intricacies of ground-borne vibrations and their impact on the surrounding environment due to a deeper comprehension of the occurrences in the frequency domain. Full article

This study proposes an improved few-shot learning model of the Siamese network residual Visual Geometry Group (VGG). This model combined with time–frequency domain transformation techniques effectively enhances the performance of across-load fault diagnosis for induction motors with limited data conditions. The proposed residual VGG-based Siamese network consists of two primary components: the feature extraction network, which is the residual VGG, and the merged similarity layer. First, the residual VGG architecture utilizes residual learning to boost learning efficiency and mitigate the degradation problem typically associated with deep neural networks. The employment of smaller convolutional kernels substantially reduces the number of model parameters, expedites model convergence, and curtails overfitting. Second, the merged similarity layer incorporates multiple distance metrics for similarity measurement to enhance classification performance. For cross-domain fault diagnosis in induction motors, we developed experimental models representing four common types of faults. We measured the vibration signals from both healthy and faulty models under varying loads. We then applied the proposed model to evaluate and compare its effectiveness in cross-domain fault diagnosis against conventional AI models. Experimental results indicate that when the imbalance ratio reached 20:1, the average accuracy of the proposed residual VGG-based Siamese network for fault diagnosis across different loads was 98%, closely matching the accuracy of balanced and sufficient datasets, and significantly surpassing the diagnostic performance of other models. Full article

This study evaluates the dynamic performance of a reference footbridge under human–structure interaction (HSI) effects using real-time hybrid simulation (RTHS). The footbridge, designed with precise multi-axial dynamic sensitivity, is tested under pedestrian gait velocities of 1.20, 1.50, and 1.80 $\mathrm{m}·{\mathrm{s}}^{-1}$. The RTHS framework involves an analytical continuous model of the footbridge as a numerical substructure and real human gait loads as the experimental substructure. The results reveal significant dynamic coupling between pedestrian-induced loads and the responses of the structure. Lateral vibrations exhibit a fundamental frequency of approximately 1.0 Hz, whereas vertical vibrations peaked near 2.0 Hz. Dynamic synchronization, particularly at higher gait velocities, amplified the structural vibrations, with lateral loading increasing by up to 300% in the middle span. Vertical loads show substantial amplification and attenuation depending on gait velocity and footbridge location. Lateral accelerations display a dispersion of approximately 15.0%, whereas vertical accelerations showed higher variability, with dispersions reaching up to 20%. The RTHS technique demonstrates high fidelity and accuracy, with global errors below 2.95% and delays of less than 2.10 ms across all evaluated directions. These results emphasize the critical importance of accounting for HSI effects in the design of pedestrian footbridges because human-induced vibrations can significantly impact structural serviceability and user comfort. This study offers important insights into optimizing footbridge design to mitigate the risks of excessive vibrations and ensure both safety and functionality under typical pedestrian loads. Full article

Traditional bearing fault diagnosis methods struggle to effectively extract distinctive, domain-invariable characterizations from one-dimensional vibration signals of high-speed train (HST) bearings under variable load conditions. A deep migration fault diagnosis method based on the combination of a domain-adversarial network and signal reconstruction unit (CRU) is proposed for this purpose. The feature extraction module, which includes a one-dimensional convolutional (Cov1d) layer, a normalization layer, a ReLU activation function, and a max-pooling layer, is integrated with the CRU to form a feature extractor capable of learning key fault-related features. Additionally, the fault identification module and domain discrimination module utilize a combination of fully connected layers and dropout to reduce model parameters and mitigate the risk of overfitting. It is experimentally validated on two sets of bearing datasets, and the results show that the performance of the proposed method is better than other diagnostic methods under cross-load conditions, and it can be used as an effective cross-load bearing fault diagnosis method. Full article

Medial gastrocnemius silent contractures (MGSCs) are prevalent, notably impacting functional status and increasing the risk of foot and ankle disorders, especially among aging populations. Although traditionally managed by podiatrists and physiotherapists, the role of rehabilitation nursing in addressing MGSCs is gaining recognition. This paper elucidates the contributions of rehabilitation nursing to the functional rehabilitation of MGSC patients and underscores its vital role within the multidisciplinary team. Initially, the paper defines the clinical and physiological characteristics of MGSCs and their implications in foot and ankle disorders. It then meticulously explores rehabilitation nursing interventions—including personalized stretching regimens, vibration therapy, balance exercises, and judicious footwear selection—emphasizing their efficacy in enhancing muscle flexibility, joint mobility, and postural stability. The emphasis is on patient-centered approaches and education to foster treatment adherence and positive rehabilitation outcomes. The significance of interdisciplinary collaboration is highlighted, focusing on how rehabilitation nursing optimizes patient care and mitigates complications. The paper advocates for recognizing and integrating rehabilitation nursing in managing MGSC-related disorders, emphasizing its importance in achieving successful functional outcomes. Full article

Collisions of over-height vehicles with low clearance bridges is commonly encountered worldwide. They have caused damage to bridge structures, interruption to traffic, injuries or even fatalities to road users. To mitigate such risks, passive systems that involve warning gantries, flashing lights and illuminated signage are commonly installed. Semi-active systems using laser- or infrared-based detection systems in conjunction with visual warnings have been implemented. Nevertheless, some drivers ignore these visual warnings and collisions continue to occur. This paper presents a novel concept for a collision prevention system, which makes use of a series of sensor-activated, motorized rumble strips. These rumble strips span across a certain distance ahead of a low clearance bridge. When an over-height vehicle is detected, a mechanism is triggered which elevates the rumble strips. The noise and vibrations produce a vigorous alert to the offending driver. They also increase effective friction of the road surface, thus assisting to slow down the vehicle and shorten the stopping distance. The strips will be lowered after a certain time has elapsed, thus minimizing their effects on other vehicles. This article presents a conceptual framework and quantifies the vibration and noise caused by rumble strips in road tests. Road tests indicated that the vibration level typically exceeded 1 g and noise level reached approximately 90 dB in the cabin of a 3.5-ton truck. Fabrication of a proof-of-concept mechanized rumble strip model was presented and verified in an outdoor environment. The circuitry and mechanical design, and requirements in actual implementation, are discussed. The proposed event-triggered rumble strip system could significantly mitigate over-height vehicle collisions that cause major disruptions and injuries worldwide. Further works, including a comprehensive road test involving various types of vehicles, are envisaged. Full article

Boar semen is commonly used in artificial insemination (AI) for pig breeding, but its quality can be negatively affected by liquid preservation and transportation, leading to reduced fertility rates. Vibration and temperature fluctuations are critical factors that significantly impact semen quality during storage and transportation, influencing the success rate of AI procedures. Betaine, a naturally occurring compound known for its role in maintaining male fertility, demonstrates potential for improving the preservation and transportation of liquid-preserved boar sperm. The present study demonstrated that betaine supplementation in the semen extenders at 0.5 mg/mL had a significant protective effect on boar sperm motility during storage at 17 °C for 3 to 5 days. During road transportation, 2.5 mg/mL betaine showed significant protective effects on boar sperm progressive motility, while 0.4 mg/mL betaine notably improved boar sperm mitochondrial activity and antioxidant capacity, and reduced lipid peroxidation damage. Simulation models also demonstrated that betaine supplementation increased the proportion of sperm displaying progressive motility and possessing intact acrosomes, regardless of the storage temperature (17 °C or 25 °C), and effectively mitigated the damage caused by vibration at a speed of 200 r/min. Overall, supplementing liquid-preserved boar semen extenders with betaine shows promise in mitigating damage to sperm quality during storage and transportation. Full article

The vibrations that a shaft suffers when rotating affect both the friction and subsequent wear of the shaft. The main objective of this paper is to present an academic and experimental prototype that allows controlling the vibrations of a rotating shaft through magnetic levitation. The control was carried out with a microcontroller, electromagnets, and proximity sensors. Other sensors and regulators were also added to the model that allow parameterization and obtaining data for subsequent analysis. According to the experiments performed in the laboratory, the proposed digital controller is capable of mitigating vibrations of a rotating shaft. This theoretical-practical methodology enables students to acquire extensive knowledge of mechanics, electronics, computing, and control in an effective manner. Although this paper presents only an academic prototype, the same technology could be applied to a wide variety of mechanisms that require rotation shafts with friction problems. Full article

Silicon carbide particle-reinforced aluminum matrix (SiCp/Al) composites are significant lightweight metal matrix composites extensively utilized in precision instruments and aerospace sectors. Nevertheless, the inclusion of rigid SiC particles exacerbates tool wear in mechanical machining, resulting in a decline in the quality of surface finishes. This work undertakes a comprehensive investigation into the problem of tool wear in SiCp/Al composite materials throughout the machining process. Initially, a comprehensive investigation was conducted to analyze the effects of cutting velocity v_c, feed per tooth f_z, milling depth a_p, and milling width a_e on tool wear during high-speed milling under SCCO₂-MQL (Supercritical Carbon Dioxide Minimum Quantity Lubrication) ultrasonic vibration conditions. The results show that under the condition of SCCO₂-MQL ultrasonic vibration, proper control of milling parameters can significantly reduce tool wear, extend tool service life, improve machining quality, and effectively reduce blade breakage and spalling damage to the tool, reduce abrasive wear and adhesive wear, and thus significantly improve the durability of the tool. Furthermore, a prediction model for tool wear was developed by employing the orthogonal test method and multiple linear regression. The model’s relevance and accuracy were confirmed using F-tests and t-tests. The results show that the model can effectively predict tool wear, among which cutting velocity v_c and feed rate f_z are the key parameters affecting the prediction accuracy. Finally, a genetic algorithm was used to optimize the milling parameters, and the optimal parameter combination (v_c = 60.00 m/min, f_z = 0.08 mm/z, a_p = 0.20 mm) was determined, and the optimized milling parameters were tested. Empirical findings suggest that the careful selection of milling parameters can significantly mitigate tool wear, extend the lifespan of the tool, and enhance the quality of the surface. This work serves as a significant point of reference for the processing of SiCp/Al composite materials. Full article

This paper endeavors to tackle the issue of horizontal vibrations encountered in high-speed and ultra-high-speed elevator cabins during operation. Given the limitations of traditional passive-control guide shoes in effectively mitigating these vibrations and the complexity and cost associated with active control systems, a novel approach involving passive-control rolling guide shoes (PCRGS) integrated with hydraulic damping is explored. The PCRGS incorporates a hydraulic actuator and hydraulic damping, which can be modeled by a mechanical and hydraulic co-simulation model using AMESim2020 software. The simulation reveals a substantial reduction in cabin vibrations equipped with PCRGS. Specifically, under pulse excitation, the reduction ranges from 26.2% to 27.5%; under white noise excitation, it varies between 14.3% and 17.1%; and under sine wave excitation, the reduction spans 21.2% to 24.1%. Notably, the system meets the stringent ‘Excellent’ (<=0.07 m/s²) performance criteria under sine wave excitation at lower frequencies, signifying its high effectiveness. These findings not only underscore the potential of hydraulic passive-control guide shoes in mitigating elevator vibrations but also provide invaluable guidance for their further development and refinement. Full article