Search Results (868)

This study presents a comparative analysis of two double-toggle drive systems for jaw crushers that are tension based and compression based (this refers to the way in which the connecting rod is mechanically stressed within the drive mechanism), with the objective of identifying the optimal configuration from both kinematic and functional perspectives. Jaw crushers play a critical role in the extractive industry, and their performance is strongly influenced by the geometry and positioning of the drive mechanism. A theoretical approach based on mathematical modeling and numerical simulation was applied to a real constructive model (SMD-117), assessing variations in the linear velocity of the moving links as a function of mechanism placement. The study employed Mathcad 15, Roberts Animator, and GIM (Graphical Interactive Mechanisms) 2025.4 software to perform calculations and simulate motion. Results revealed a sinusoidal velocity pattern with significant differences between the two systems: the tension-based drive achieves peak velocities at the beginning of the angular variation interval, while the compression-based system reaches its maximum toward the end. Link C consistently exhibits higher velocities than link E, indicating increased mechanical stress. Polar graphic analysis identified critical velocity angles, and simulations confirmed the model’s validity with a maximum error of just 1.79%. The findings emphasize the importance of selecting an appropriate drive system to enhance performance, durability, and energy efficiency, offering concrete recommendations for equipment design and operation. Full article

Most existing assembly accuracy analysis methods focus on rigid assemblies or assume assemblies to be rigid bodies, neglecting the influence of assembly deformation in weak rigid components (WRCs) such as thin-walled structures, cantilever structures, etc. As a result, the assembly accuracy analysis becomes inaccurate, and the accuracy of key components cannot be effectively controlled. This may lead to serious issues such as forced assembly, repair, and rework. To address these problems, this study proposes a rigid–flexible coupling-based assembly accuracy analysis method for WRCs. The stiffness matrix and assembly deformation of WRCs are calculated, and by coupling assembly deformation with other assembly deviations, a rigid–flexible coupling assembly accuracy data model is established. This model incorporates multiple deviation sources, including assembly process variations, design tolerances, and assembly deformations. Assembly deviation transfer modeling and accumulation calculation methods for WRCs are investigated, enabling assembly accuracy simulation and statistical analysis. A case study on WRC assembly accuracy analysis is conducted, and the results demonstrate that the proposed method improves the accuracy of assembly analysis for WRCs, verifying its reliability. Full article

Line laser measurement, as a typical method of laser triangulation, makes the acquisition of 3D tooth-surface data more accurate, efficient, and informative. Thus, a line laser 3D measurement model of gears is established, and a specialized polyhedral artifact with specific geometric features is invented to determine the pose parameters of the line laser sensor in measuring space. Based on this, a single-spindle gear-measuring instrument is developed and a series of experimental studies are conducted for gears with different module and flank directions in this instrument, including profile deviation, helix deviation, pitch deviation, topological deviation, etc. A comparative experiment with traditional contact measurement methods validates the correctness of the methods mentioned in this paper for the accurate evaluation of tested gears. In further research, the mining and utilization of big data obtained from the line laser 3D measurement of gears will be an important topic. Full article

Electrohydrodynamic (EHD) printing offers mask-free, high-resolution deposition across a broad range of ink viscosities, yet combining void-free filling of high-aspect-ratio through-glass vias (TGVs) with ultrafine drop-on-demand (DOD) line printing on the same platform requires balancing conflicting requirements: for example, high field strengths to drive ink into deep and narrow vias; sufficiently high ink viscosity to prevent gravity-induced leakage; and stable meniscus dynamics to avoid satellite droplets and charge accumulation on the glass surface. By coupling electrostatic field analysis with transient level-set simulations, we establish a dimensionless regime map that delineates stable cone-jetting regime; these predictions are validated by high-speed imaging and surface profilometry. Operating within this window, the platform achieves complete, void-free filling of 200 µm × 1.52 mm TGVs and continuous 10 µm-wide traces in a single print pass. Demonstrating its capabilities, we fabricate transparent Ku-band substrate-integrated waveguide antennas on borosilicate glass: the printed vias and arc feed elements exhibit a reflection coefficient minimum of −18 dB at 14.2 GHz, a −10 dB bandwidth of 12.8–16.2 GHz, and an 8 dBi peak gain with 37° beam tilt, closely matching full-wave predictions. This physics-driven, all-in-one EHD approach provides a scalable route to high-performance, glass-integrated RF devices and transparent electronics. Full article

Environmental noise is inevitable during seismic data acquisition, with major sources including heavy machinery, rivers, wind, and other environmental factors. During field data acquisition, it is important to assess the impact of environmental noise and evaluate data quality. In subsequent seismic data processing, these noise components also need to be eliminated. Accurate identification of noise traces facilitates rapid quality control (QC) during fieldwork and provides a reliable basis for targeted noise attenuation. Conventional environmental noise identification primarily relies on amplitude differences. However, in seismic data, high-amplitude signals are not necessarily caused by environmental noise. For example, surface waves or traces near the shot point may also exhibit high amplitudes. Therefore, relying solely on amplitude-based criteria has certain limitations. To improve noise identification accuracy, we use the Mel-spectrogram to extract features from seismic data and construct the dataset. Compared to raw time-series signals, the Mel-spectrogram more clearly reveals energy variations and frequency differences, helping to identify noise traces more accurately. We then employ a Vision Transformer (ViT) network to train a model for identifying noise in seismic data. Tests on synthetic and field data show that the proposed method performs well in identifying noise. Moreover, a denoising case based on synthetic data further confirms its general applicability, making it a promising tool in seismic data QC and processing workflows. Full article

This article presents a comparative study of the storage of energy produced by photovoltaic panels by means of two types of batteries: Lead–Acid and Lithium-Ion batteries. The work involved the construction of a model in MATLAB-Simulink for controlling the loading/unloading of storage batteries with energy produced by photovoltaic panels through a buck-type DC-DC convertor, controlled by means of the MPPT algorithm implemented through the method of incremental conductance based on a MATLAB function. The program for the MATLAB function was developed by the author in the C++ programming environment. The MPPT algorithm provides maximum energy transfer from the photovoltaic panels to the battery. The electric power taken over at a certain moment by Lithium-Ion batteries in photovoltaic panels is higher than the electric power taken over by Lead–Acid batteries. Two types of batteries were successively used in this model: Lead–Acid and Lithium-Ion batteries. Based on the results being obtained and presented in this work it may be affirmed that the storage battery Lithium-Ion is more performant than the Lead-Acid storage battery. At the Laboratory of Electrical Machinery and Drives of the Engineering Faculty of Bacau, an experimental stand was built for a storing system for electric energy produced by photovoltaic panels. For controlling DC-DC buck-type convertors, a program was developed in the programming environment Arduino IDE for implementing the MPPT algorithm for incremental conductance. The simulation part of this program is similar to that of the program developed in C++. Through conducting experiments, it was observed that, during battery charging, along with an increase in the charging voltage, an increase in the filling factor of the PWM signal controlling the buck DC-DC convertor also occurred. The findings of this study may be applicable to the storage of battery-generated electrical energy used for supplying electrical motors in electric cars. Full article

Rotating compressors are foundational in various industrial processes, particularly in the oil-and-gas sector, where reliable fault detection is crucial for maintaining operational continuity. While Graph Attention Network (GAT) frameworks are widely available, this study advances the state of the art by introducing a Bayesian GAT method specifically tailored for vibration-based compressor fault diagnosis. The approach integrates domain-specific digital-twin simulations built with Rotordynamic software (1.3.0), and constructs dual adjacency matrices to encode both physically informed and data-driven sensor relationships. Additionally, a hybrid forecasting-and-reconstruction objective enables the model to capture short-term deviations as well as long-term waveform fidelity. Monte Carlo dropout further decomposes prediction uncertainty into aleatoric and epistemic components, providing a more robust and interpretable model. Comparative evaluations against conventional Long Short-Term Memory (LSTM)-based autoencoder and forecasting methods demonstrate that the proposed framework achieves superior fault-detection performance across multiple fault types, including misalignment, bearing failure, and unbalance. Moreover, uncertainty analyses confirm that fault severity correlates with increasing levels of both aleatoric and epistemic uncertainty, reflecting heightened noise and reduced model confidence under more severe conditions. By enhancing GAT fundamentals with a domain-tailored dual-graph strategy, specialized Bayesian inference, and digital-twin data generation, this research delivers a comprehensive and interpretable solution for compressor fault diagnosis, paving the way for more reliable and risk-aware predictive maintenance in complex rotating machinery. Full article

Background: During infection with the cytomegalovirus (CMV), the membrane system of the infected cell is remodelled into a megastructure called the assembly compartment (AC). These extensive changes may involve the manipulation of the host cell proteome by targeting a pleiotropic function of the cell such as ubiquitination (Ub). In this study, we investigate whether the Ub system is required for the establishment and maintenance of the AC in murine CMV (MCMV)-infected cells Methods: NIH3T3 cells were infected with wild-type and recombinant MCMVs and the Ub system was inhibited with PYR-41. The expression of viral and host cell proteins was analyzed by Western blot. AC formation was monitored by immunofluorescence with confocal imaging and long-term live imaging as the dislocation of the Golgi and expansion of Rab10-positive tubular membranes (Rab10 TMs). A cell line with inducible expression of hemagglutinin (HA)-Ub was constructed to monitor ubiquitination. siRNA was used to deplete host cell factors. Infectious virion production was monitored using the plaque assay. Results: The Ub system is required for the establishment of the infection, progression of the replication cycle, viral gene expression and production of infectious virions. The Ub system also regulates the establishment and maintenance of the AC, including the expansion of Rab10 TMs. Increased ubiquitination of WASHC1, which is recruited to the machinery that drives the growth of Rab10 TMs, is consistent with Ub-dependent rheostatic control of membrane tubulation and the continued expansion of Rab10 TMs. Conclusions: The Ub system is intensively utilized at all stages of the MCMV replication cycle, including the reorganization of the membrane system into the AC. Disruption of rheostatic control of the membrane tubulation by ubiquitination and expansion of Rab10 TREs within the AC may contribute to the development of a sufficient amount of tubular membranes for virion envelopment. Full article

The electrification of road vehicles is a relatively mature sector, while other areas of mobility, such as construction machinery, are just beginning their transition to electric solutions. This work presents the design and realization of an integrated drive system specifically developed for retrofitting fan drives in heavy machinery, like bulldozers and tractors, utilizing existing 48 VDC batteries. By replacing or complementing internal combustion and hydraulic technologies with electric solutions, significant advantages in efficiency, reduced environmental impact, and versatility can be achieved. Focusing on the fan drive system addresses the critical challenge of thermal management in high ambient temperatures and harsh environments, particularly given the high current requirements for $3\mathrm{k}\mathrm{W}$-class applications. A sensorless architecture has been selected to enhance reliability by eliminating mechanical position sensors. The developed fan drive has been extensively tested both on a braking bench and in real-world applications, demonstrating its effectiveness and robustness. Future work will extend this prototype to electrify additional onboard hydraulic motors in these machines, further advancing the electrification of heavy-duty equipment and improving overall efficiency and environmental impact. Full article

Industrial equipment fault diagnosis faces dual challenges: significant data distribution discrepancies caused by diverse operating conditions impair generalization capabilities, while underutilized spatio-temporal information from multi-source data hinders feature extraction. To address this, we propose a spatio-temporal collaborative perception-driven feature graph construction and topology mining methodology for variable-condition diagnosis. First, leveraging the operational condition invariance and cross-condition consistency of fault features, we construct fault feature graphs using single-source data and similarity clustering, validating topological similarity and representational consistency under varying conditions. Second, we reveal spatio-temporal correlations within multi-source feature topologies. By embedding multi-source spatio-temporal information into fault feature graphs via spatio-temporal collaborative perception, we establish high-dimensional spatio-temporal feature topology graphs based on spectral similarity, extending generalized feature representations into the spatio-temporal domain. Finally, we develop a graph residual convolutional network to mine topological information from multi-source spatio-temporal features under complex operating conditions. Experiments on variable/multi-condition datasets demonstrate the following: feature graphs seamlessly integrate multi-source information with operational variations; the methodology precisely captures spatio-temporal delays induced by vibrational direction/path discrepancies; and the proposed model maintains both high diagnostic accuracy and strong generalization capacity under complex operating conditions, delivering a highly reliable framework for rotating machinery fault diagnosis. Full article

Tunnel construction poses significant safety challenges due to confined spaces, limited visibility, and the dynamic movement of labourers and machinery. This study addresses a critical gap in real-time, bidirectional proximity monitoring by developing and validating a prototype early-warning system that integrates real-time location systems (RTLS) with long-range (LoRa) wireless communication and ultra-wideband (UWB) positioning. The system comprises Arduino nano microcontrollers, organic light-emitting diode (OLED) displays, and piezo buzzers to detect and signal proximity breaches between workers and equipment. Using an action research approach, three pilot case studies were conducted in a simulated tunnel environment to test the system’s effectiveness in both static and dynamic risk scenarios. The results showed that the system accurately tracked proximity and generated timely alerts when safety thresholds were crossed, although minor delays of 5–8 s and slight positional inaccuracies were noted. These findings confirm the system’s capacity to enhance situational awareness and reduce reliance on manual safety protocols. The study contributes to the tunnel safety literature by demonstrating the feasibility of low-cost, real-time monitoring solutions that simultaneously track labour and machinery. The proposed RTLS framework offers practical value for safety managers and informs future research into automated safety systems in complex construction environments. Full article

The two-stage proportional valve is a key control component in heavy-duty equipment, where its signal-flow characteristics critically influence operational performance. This study proposes an innovative flow characteristic regulation method using variable-gain feedback grooves. Unlike conventional throttling notch optimization, the core mechanism actively adjusts pilot–main valve mapping through feedback groove shape and area gain adjustments to achieve the desired flow curves. This approach avoids complex throttling notch issues while retaining the valve’s high dynamics and flow capacity. Mathematical modeling elucidated the underlying mechanism. Subsequently, trapezoidal and composite feedback grooves are designed and investigated via simulation. Finally, composite feedback groove spools tailored to construction machinery operating conditions are developed. Comparative experiments demonstrate the following: (1) Pilot–main mapping inversely correlates with area gain; increasing gain enhances micro-motion control, while decreasing gain boosts flow gain for rapid actuation. (2) This method does not significantly increase pressure loss or energy consumption (measured loss: 0.88 MPa). (3) The composite groove provides segmented characteristics; its micro-motion flow gain (2.04 L/min/0.1 V) is 61.9% lower than conventional valves, significantly improving fine control. (4) Adjusting groove area gain and transition point flexibly modifies flow gain and micro-motion zone length. This method offers a new approach for high-performance valve flow regulation. Full article

Autonomous earthwork machinery is gaining traction as a means to boost productivity and safety on space-constrained urban sites, yet the fast-growing literature has not been fully integrated. To clarify current knowledge, we systematically searched Scopus and screened 597 records, retaining 157 peer-reviewed papers (2015–March 2025) that address autonomy, integrated control, or risk mitigation for excavators, bulldozers, and loaders. Descriptive statistics, VOSviewer mapping, and qualitative synthesis show the output rising rapidly and peaking at 30 papers in 2024, led by China, Korea, and the USA. Four tightly linked themes dominate: perception-driven machine autonomy, IoT-enabled integrated control systems, multi-sensor safety strategies, and the first demonstrations of fleet-level collaboration (e.g., coordinated excavator clusters and unmanned aerial vehicle and unmanned ground vehicle (UAV–UGV) site preparation). Advances include centimeter-scale path tracking, real-time vision-light detection and ranging (LiDAR) fusion and geofenced safety envelopes, but formal validation protocols and robust inter-machine communication remain open challenges. The review distils five research priorities, including adaptive perception and artificial intelligence (AI), digital-twin integration with building information modeling (BIM), cooperative multi-robot planning, rigorous safety assurance, and human–automation partnership that must be addressed to transform isolated prototypes into connected, self-optimizing fleets capable of delivering safer, faster, and more sustainable urban construction. Full article

To analyze the wear performance induced by rotor–stator rubbing in an aero-engine sealing structure under authentic operating conditions, a transonic rotor system with double bearing is constructed. This system incorporates the disk, shaft, blades, joint bolts, and auxiliary support structure. The system was evaluated in terms of its critical speed, vibration characteristics, component strength under operational conditions, and response characteristics in abnormal extreme scenarios. A ball screw-type feeding system is employed to achieve precise rotor–stator rubbing during rotation by controlling the coating feed. Additionally, a quartz lamp heating system is used to apply thermal loads to coating specimens, and the appropriate heat insulation and cooling measures are implemented. Furthermore, a high-frequency rubbing force test platform is developed to capture the key characteristics caused by rubbing. The test rig can conduct response tests of the system with rotor–stator rubbing and abrasion tests with tip speeds reaching 425 m/s, feed rates ranging from 2 to 2000 μm/s, and heating temperatures up to 1200 °C. Test debugging has confirmed these specifications and successfully executed rubbing tests, which demonstrate stability throughout the process and provide reliable rubbing force test results. This designed test rig and analysis methodology offers valuable insights for developing high-speed rotating machinery. Full article

Intelligent fault diagnosis for rotary machinery often suffers performance degradation under domain shifts between training and deployment environments. To address this, we propose a robust fault diagnosis framework incorporating three key components: (1) an order-frequency-based preprocessing method to normalize rotational variations, (2) a U-Net variational autoencoder (U-NetVAE) to enhance adaptation through reconstruction learning, and (3) a test-time training (TTT) strategy enabling unsupervised target domain adaptation without access to source data. Since existing works rarely evaluate under true domain shift conditions, we first construct a unified cross-domain benchmark by integrating four public datasets with consistent class and sensor settings. The experimental results show that our method outperforms conventional machine learning and deep learning models in both F1-score and recall across domains. Notably, our approach maintains an F1-score of 0.47 and recall of 0.51 in the target domain, outperforming others under identical conditions. Ablation studies further confirm the contribution of each component to adaptation performance. This study highlights the effectiveness of combining mechanical priors, self-supervised learning, and lightweight adaptation strategies for robust fault diagnosis in the practical domain. Full article