Journal of Mechanical Engineering, Automation and Control Systems
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Experimental method for determining the vibrodynamic state of embankments on high-speed railways
Get PDFThe article presents modern methods for reinforcing the embankment in the zone of the interface between the coastal bridge piers and the earth bed of the high-speed railway section. It has been established that as a result of driving reinforced concrete piles into the railway embankment, the natural vibrations of the earthwork decrease by up to 15 %. A frequency equal to the frequency of vibrations arising from the speed of high-speed railways with the help of vibrators on models of the earth bed for determining the amplitude-frequency characteristics of various design points has been created and the values of this frequency have been processed by fixing them with the help of seismometric sensors SM-3 in all design points. A significant decrease of shear at the main site after driving of reinforced concrete piles and approaching of this value to microseismic value based on the values of sensors located at the main site and at a distance of 1.5 m from the foundation is determined. It has been established that by driving reinforced concrete piles into the railway embankment, the vertical settlement of the earthwork decreases by 33 % and 50 % depending on the soil type. Also, the methodology of experimentation for the study of vibrations of the earth bed piled from different soils on high-speed railroads is given
Research on coupling dynamic characteristics and parameter influence of TBM cutterhead system
Get PDFAs an important system of TBM, the host system bears the impact of unstable load from itself and the strong load of the rock in the geological layer during operation, which causes irregular vibration of the host system, resulting in low tunneling efficiency, and is more likely to cause cutterhead cracking and component damage. To this end, with the help of analysis software such as Matlab and Ansys, the intrinsic characteristics and vibration response of the host system are studied, and the specific parameters of the vibration influencing factors are discussed. The results show that the axial displacement of the center block of the cutterhead is the largest, reaching 0.85 mm, and the longitudinal displacement value is about 2-3 times of the transverse displacement; in the design stage, the mass of the central block should be controlled in the range of 50 %-55 %, and the rest of the cutterhead should be controlled in the range of 12.5 %-13.5 %; the vibration is the smallest under the uniform layout of the gear, the fluctuation of the solid short shaft connection of the motor is relatively stable, and the maximum vibration value does not exceed 3.5e-2 mm
Feature data analysis of dance movements by motion capture
Get PDFMotion capture technology has been applied in more and more fields, but the research in the field of dance is relatively rare. In order to combine motion capture technology with dance research, better understand the characteristics of dance movements, and provide support for their digital analysis, this paper mainly studied the application of a motion capture technology called Kinect in the analysis of dance movement feature data. The skeleton data of different dance movements was first collected based on Kinect v2, and then the collected data was analyzed using a spatio-temporal graph convolutional network (ST-GCN). On the basis of the original ST-GCN, the multi-branch structure was adopted to realize co-occurrence feature learning, and the bone length feature and direction feature were introduced to further enrich the feature data. Experiments were carried out on the NTU RGB+D and dance datasets. It was found that the improved ST-GCN had better performance than other current motion classification approaches on the NTU RGB+D. The top-1 accuracy for cross-subject (CS) and cross-view (CV) was 92.4 % and 96.7 %, respectively, and the average accuracy of different dance movements for the dance dataset was 96.035. The findings confirm the effectiveness of the proposed approach in the analysis of dance movement feature data, and it can be applied in the actual research of dance movements
Magnetoelastic oscillation of current-carrying plates in an alternating magnetic field
Get PDFModern technological advancements, particularly in micro- and nanoelectronics, aerospace engineering, sensor systems, and robotics, necessitate a deeper understanding of how structural elements behave under various physical influences. One significant and relevant phenomenon is magnetoelastic interaction, which involves how the mechanical behavior of current-carrying elastic bodies is affected not only by external loads but also by internal electromagnetic processes. Current-carrying plates, commonly utilized in micro- and nanoelectronics, respond to external fields by altering their stress-strain states. To accurately model these processes, an integrated approach is required that considers mechanical, electromagnetic, and thermal effects caused by electrical currents. This paper focuses on the mathematical modeling and numerical study of transverse magnetoelastic oscillations in thin current-carrying plates subjected to an alternating magnetic field. The problem is formulated considering electromagnetic interactions, geometric nonlinearity, and external alternating currents. A comprehensive system of equations is developed that includes the equations of motion, Maxwell's equations, and the heat equation with Joule heating sources. For the numerical solution, the finite difference method using the Newmark scheme and discrete orthogonalization techniques are applied. Graphs illustrating stress and strain distributions are presented, and the effects of magnetic field frequency and external current on the system’s behavior are analyzed. This research is vital for designing reliable components in micro- and nano-electronics and aviation
Numerical simulation of a gas-flotation oil–water hydrocyclone separator
Get PDFEfficient oil–water separation of produced fluids from high water-cut oilfields requires significant improvement in hydrocyclone separation performance. In this work, computational fluid dynamics simulations were applied to analyze the influence of integrating gas flotation with hydrocyclone separation. To describe the internal flow behavior and the distribution of oil droplets in gas-assisted operation, the Mixture multiphase model together with the Realizable k-ε turbulence model was utilized. Based on a conventional liquid-liquid hydrocyclone, a porous medium region was incorporated into the large cone section to represent microporous walls for microbubble injection, thereby achieving the coupling of flotation and hydrocyclone separation. The results show that gas injection enhanced the separation efficiency from 83.56 % to 95.96 %. Moreover, microbubble size exhibited a pronounced influence on separation performance: smaller bubbles facilitated better oil-water separation. The optimal performance was obtained with an air bubble diameter of 5 μm, where the separation efficiency reached 97.73 %
Kinematic synthesis of a cam-follower mechanism of a novel internal combustion engine
Get PDFThis paper presents a kinematic synthesis of a groove-type disk cam that directly drives sliders in a novel internal-combustion engine architecture. The synthesis is formulated in an invariant (normalized) space and enforces zero acceleration at phase boundaries while embedding a quasi-constant-velocity segment in the mid-portion of the compression (retraction) phase. An arbitrary shaping function is introduced to generate a family of admissible motion laws; a constrained optimization (series truncated to four terms) minimizes the peak acceleration under a prescribed bound on velocity, yielding a PLM with a quasi-constant-velocity interval of approximately 39 % of the kinematic cycle (±5 %). The synthesized retraction law is paired with a sinusoidal approach (power) law to ensure zero endpoint accelerations for both phases. Cam profiles for the working and return strokes are constructed; maximum pressure angles remain within admissible limits across examined phase splits, including an experimental 65°/25° case. Compared with the sinusoidal baseline, the synthesized law retains a similar acceleration constant but reduces the velocity constant by approximately 31 %, indicating lower inertial loading and milder end-conditions that are favorable for mixture preparation and bearing lubrication. The results provide a compact, implementable route to motion programming for cam-driven reciprocators in internal-combustion engines and establish feasibility for multi-cylinder layouts
Small targets detection in low-resolution remote sensing images based on super-resolution joint optimization
Get PDFWhile convolutional neural networks have driven remarkable progress in remote sensing object detection, persistent challenges remain in detecting small targets within low-resolution imagery due to their limited pixel representation and feature degradation during hierarchical downsampling. To address this, this study proposed the joint super-resolution and detection network (JSRDN), which synergistically optimizes SR reconstruction through task-specific detection feedback, significantly enhancing small target recognition in LR remote sensing imagery. Firstly, generator in generative adversarial network incorporates improved residual blocks, enabling enhanced perception of complex deep-level features in the SR reconstruction process. Then, a perceptual loss function is introduced into the adversarial training process, which captures perceptual discrepancies in high-level features between reconstructed images and original HR references. After that, an edge-enhancement network is designed to dynamically detect edges in intermediate features restored by the generator, prioritizing edge influence across network layers to generate discriminative features for target recognition. Furthermore, the JSRDN implements detection-driven feedback by backpropagating object recognition loss through the generator, enforcing the super-resolution process to prioritize detection-salient feature recovery. Evaluated on 64×64 low-resolution COWC datasets, JSRDN achieves 0.1819 dB peak signal-to-noise ratio (PSNR) and 7.18 % average precision (AP) improvements over the deep residual dual-attention network (DRDAN), with ablation studies and visualizations confirming its balanced optimization of reconstruction fidelity and detection-oriented feature learning. This technology can provides valuable support for small target measurement and opens new opportunities in the field
Fault diagnosis of time-varying speed gearbox based on gated recurrent dropout attention unit
Get PDFIn response to the difficulty of fault diagnosis of gearbox under time-varying speed conditions, this paper presents a novel approach for diagnosing gearbox faults in time-varying speed, utilizing an improved gate recurrent unit (GRU), which adds attention gate mechanism and cyclic dropout learning strategies on the basis of the GRU, and constructs a new model named as gated recurrent dropout attention unit (GRDAU). By introducing attention gate mechanism to realize allocating weights dynamically, focusing on key features, and enhancing GRU’s ability to capture important information. In addition, the designed cyclic dropout learning strategy reduces excessive dependence on specific hidden states by randomly discarding some hidden state information. Finally, the robustness and excellent interference suppression ability of the proposed method were verified through case analysis of a gearbox under time-varying speed, and the diagnostic accuracy of the method is as high as 99.78 %. Comparative experiments were conducted to validate its superior performance and stronger generalization ability compared to existing advanced diagnostic methods
Mathematical modeling of the rotating drum granular fill flow oscillatory stability
Get PDFDrum-type machines have become widely used in many industries for processing various granular materials. An innovative direction for significantly increasing the energy efficiency of such equipment is the use of self-oscillating working processes. Self-excitation of auto-oscillations allows you to bring into pulsating flow and activate the passive part of the intra-chamber filling and significantly enhance the interaction of granular particles with each other and with the surrounding environment. The purpose of the study is to build a mathematical model of the conditions and factors of oscillatory instability of the flow of polydisperse granular filling in the chamber of a rotating drum. The research methodology includes analytical modeling of wave processes and experimental modeling of manifestations of instability of the filling flow. The inertial mode of flow of the active part of the filling in a shear flow state is analyzed, the behavior of which is described using averaged values. Based on the results obtained, an increase in instability with an increase in the dilatancy of the medium during deformation is established and the destabilizing effect of the damping action of the fine fraction on the interaction of particles of the coarse fraction is revealed. The main scientific novelty of this study is the identification of the regularities of the unsteady motion of the oscillatory system of a filled drum. The study confirms the possibility of generating, under certain conditions, self-excitation of auto-oscillations of the intra-chamber filling, which is a decisive factor in the predicted intensification of the technological process. The results obtained are valuable for researchers and engineers involved in the study and design of innovative energy-efficient working processes of drum machines
Criticality mapping of a system in the mining industry using Bayesian network
Get PDFEffective evaluation of equipment criticality is a key concern in Engineering Asset Management, particularly in operationally intensive industries such as mining. While the concept of criticality is often subjective, it can be assessed more objectively using quantifiable indicators such as cost, downtime, and failure rate. This paper presents a data-driven approach to assess equipment-level criticality by analysing the impact of individual equipment downtimes on overall system performance. Focusing on a case study from a gold mining operation in Australia, the study demonstrates how equipment-level performance can be used to prioritise maintenance efforts and support more informed decision-making. One of the key contributions of this work lies in its integration of statistical modelling and probabilistic analysis to identify critical equipment within a system. Unlike conventional methods that often overlook uncertainty or assume uniform equipment influence, this approach quantifies the impact of individual equipment failures on system-level outcomes. The analysis treats subsystems independently, acknowledging the absence of interdependency data while still capturing meaningful insights about their relative importance. By leveraging a combination of platforms – Excel for data preprocessing, R for simulation, and Netica for network-based evaluation – the study offers a replicable and scalable methodology for criticality assessment. Sensitivity analysis within the Bayesian Network model further enhances the framework by highlighting components with the highest influence on system reliability. The outcome is a transparent, objective, and practically applicable tool for maintenance prioritisation, offering significant value in data-intensive and reliability-critical environments like mining. This paper contributes to the growing body of research focused on integrating operational data with advanced modelling techniques to improve asset performance management