This study has developed a novel measurement-based computational method that accurately determines the vertical and lateral wheel–rail contact forces transmitted from railway vehicles to the rails. A major contribution—and the first in the literature—is the analytical distribution of the total lateral wheelset force [...] Read more.

This study has developed a novel measurement-based computational method that accurately determines the vertical and lateral wheel–rail contact forces transmitted from railway vehicles to the rails. A major contribution—and the first in the literature—is the analytical distribution of the total lateral wheelset force into its outer-wheel and inner-wheel components, thereby enabling precise individual evaluation of derailment risk on each wheel in curved tracks. Analytical equations derived from Newton’s second law were first formulated to express both vertical forces and total axle lateral force directly from bogie/axle-box accelerations and suspension reactions. To eliminate the deviations caused by conventional simplifying assumptions (neglect of creep effects, wheel diameter variation, and constant contact geometry), surrogate functions and distribution equations sensitive to curve radius, vehicle speed, and cant deficiency were introduced for the first time and seamlessly integrated into the equations. Validation was performed using the Istanbul Tramway multibody model in SIMPACK 2024x.2, with the equations implemented in MATLAB/Simulink R2024b. Excellent agreement with SIMPACK reference results was achieved on straight tracks and curves, after regression-based calibration of the surrogate functions. Although the method requires an initial regression calibration within a simulation environment, it relies exclusively on measurable parameters, ensuring low cost, full compatibility with existing vehicle sensors, and genuine suitability for real-time monitoring. Consequently, it supports predictive maintenance and proactive safety management while overcoming the practical limitations of instrumented wheelsets and offering a robust, fleet-scalable alternative for the railway industry.Full article

The International Energy Agency (IEA) asserts that worldwide electricity demand is rising exponentially every year. Energy storage is the cornerstone of electricity demand. Gravity-based energy storage systems represent the optimum alternative for energy storage systems. They offer zero carbon emission, environmental sustainability, cost-effectiveness, [...] Read more.

The International Energy Agency (IEA) asserts that worldwide electricity demand is rising exponentially every year. Energy storage is the cornerstone of electricity demand. Gravity-based energy storage systems represent the optimum alternative for energy storage systems. They offer zero carbon emission, environmental sustainability, cost-effectiveness, geographical flexibility, long-duration storage, and scalability ranging from 0.5 to 10 GWh. This research introduces a novel design to confirm the workability of the gravity energy storage model. It validates the feasibility of the system through the drive train setup. The drive train model involves storing potential energy by elevating the stack weight using solar photovoltaic input and releasing the weight to generate electrical energy using the gravitational field. The gravity motion is theoretically proven by the mathematical analysis, drive train control system transfer function model, and golden ratio-based design. Solidworks simulation model enhances the working of the drive train setup. Through hardware iterative experimental results with different load profiles, validate the performance metrics. The gravity energy storage system’s feasibility is demonstrated by its scalability in comparison with battery energy systems. Gravity-based energy storage is the best option for utility-scale renewable energy grid integration, since it has a low energy density, medium and large capacity, long-lasting storage, and high scalability.Full article

Current research on tower crane control lacks high-fidelity models and fails to account for the coupling effects between the tower crane structure and the hoisting and luffing systems. A new dynamic analysis platform based on the flexible multibody system theory is proposed in [...] Read more.

Current research on tower crane control lacks high-fidelity models and fails to account for the coupling effects between the tower crane structure and the hoisting and luffing systems. A new dynamic analysis platform based on the flexible multibody system theory is proposed in this investigation for the tower crane which contains a large-scale steel structure and hoisting mechanisms undergoing large displacements and large deformations. The Arbitrary Lagrangian–Eulerian–Absolute Nodal Coordinate Formulation (ALE–ANCF) cable element was employed to model the varying length of the steel rope in the hoisting mechanisms. Nonlinear kinetic equations were used to describe the motion of a luffing trolley. The solving strategy of the system’s dynamical equations are presented. Two different trajectories were tested. Simulation results demonstrate the feasibility and rationality of the proposed dynamic analysis platform. The primary conclusion is that this platform serves as a reliable and high-fidelity testbed for developing and evaluating advanced control algorithms under realistic dynamic conditions, thereby providing a dependable tool for both research and engineering applications.Full article

In this paper, the inductance of a sensorless PMSM (Permanent Magnet Synchronous Motor) drive system equipped with a periodic load torque compensator based on a wavelet denoising and least-order observer with time-delay compensation is estimated in real-time. In a sensorless PMSM system with [...] Read more.

In this paper, the inductance of a sensorless PMSM (Permanent Magnet Synchronous Motor) drive system equipped with a periodic load torque compensator based on a wavelet denoising and least-order observer with time-delay compensation is estimated in real-time. In a sensorless PMSM system with constant load torque, the magnetically saturated inductance value remains constant. This constant inductance error causes minor performance degradation, such as a constant rotor position estimation error and non-optimal torque current, but it does not introduce a speed estimation error. Conversely, in a sensorless PMSM motor system subjected to periodic load torque, the magnetically saturated inductance error fluctuates periodically. This fluctuation leads to periodic variations in both the estimated position error and the speed error, ultimately degrading the load torque compensation performance. This paper applies the maximum energy-to-Shannon entropy criterion for the optimal selection of the mother wavelet in the wavelet transform to remove the motor signal noise and achieve more accurate inductance estimation. Additionally, the coherence and correlation theory is proposed to address the time delay in the least-order observer and improve the time delay. A self-saturation compensation method is also proposed to minimize periodic speed fluctuations and improve control accuracy through inductance parameter estimation. Finally, experiments were conducted on a sensorless PMSM drive system to verify the inductance estimation performance and validate the effectiveness of vibration reduction.Full article

Data loss is a recurring and critical issue in Structural Health Monitoring (SHM) systems, often arising from a range of factors including sensor malfunction, communication breakdown, and exposure to adverse environmental conditions. Such interruptions in data availability can significantly compromise the accuracy and [...] Read more.

Data loss is a recurring and critical issue in Structural Health Monitoring (SHM) systems, often arising from a range of factors including sensor malfunction, communication breakdown, and exposure to adverse environmental conditions. Such interruptions in data availability can significantly compromise the accuracy and reliability of structural performance assessments, thereby hindering effective decision-making in safety evaluation and maintenance planning. In this study, a novel deep learning-based framework is proposed for data reconstruction in SHM, employing a hybrid architecture that integrates one-dimensional convolutional neural networks (1D-CNNs) with recurrent neural networks (RNNs). By combining these complementary strengths, the hybrid 1D-CNN–RNN model demonstrates superior capacity for accurate signal reconstruction. A real-world case study was conducted using vibration data from the Trai Hut Bridge in Vietnam. Five network configurations with varying depths were examined under single- and multi-channel loss scenarios. The results confirm that the method can accurately reconstruct lost signals. For single-channel loss, the best configuration achieved an MAE = 0.019 m/s² and R² = 0.987, while for multi-channel loss, a deeper network yielded an MAE = 0.044 m/s² and R² = 0.974. Furthermore, the model exhibits robust and stable performance even under more demanding multi-channel data loss conditions, highlighting its resilience to practical operational challenges. The results demonstrate that the proposed CNN–RNN framework is accurate, robust, and adaptable for practical SHM data reconstruction applications.Full article

A single-plunger two-dimensional electro-hydraulic pump is an integrated unit in which a two-dimensional plunger pump is embedded inside the rotor of a permanent magnet synchronous motor, significantly improving the power density and power-to-weight ratio of electro-hydraulic pumps. The pursuit of a higher power-to-weight [...] Read more.

A single-plunger two-dimensional electro-hydraulic pump is an integrated unit in which a two-dimensional plunger pump is embedded inside the rotor of a permanent magnet synchronous motor, significantly improving the power density and power-to-weight ratio of electro-hydraulic pumps. The pursuit of a higher power-to-weight ratio has made high-speed operation and high-pressure output persistent research priorities. However, during the iterative design process of electro-hydraulic pumps, cavitation has been identified as a common issue, leading to difficulties in oil suction and even severe backflow. Based on the structure and motion characteristics of the single-plunger two-dimensional electro-hydraulic pump, a CFD numerical model was established to analyze the influence of different working conditions on the cavitation characteristics inside the pump. The study shows that cavitation mainly occurs in the plunger chamber, the distribution groove, and the triangular damping groove. The location and intensity of cavitation are directly reflected by the gas volume fraction. The simulation analysis of variable operating conditions has verified that suction pressure and rotational speed have a significant impact on cavitation—an increase in suction pressure can effectively suppress cavitation, while an increase in rotational speed will exacerbate cavitation development. Specifically, the non-cavitation working boundary of this type of pump was determined through theoretical derivation, and the coupling relationship between critical suction pressure and critical speed was clarified. This work provides an important theoretical basis for the optimization design of the new integrated electro-hydraulic pump.Full article

The traditional A* algorithm performs well in single-map environments, but it is prone to path redundancy and obstacle handling delays in complex multi-map collaborative scenarios, making it unsuitable for the characteristics of multi-environment maps. To address these challenges of traditional A* algorithms, this [...] Read more.

The traditional A* algorithm performs well in single-map environments, but it is prone to path redundancy and obstacle handling delays in complex multi-map collaborative scenarios, making it unsuitable for the characteristics of multi-environment maps. To address these challenges of traditional A* algorithms, this paper proposes a multi-environment map rescue robot path planning method based on an improved A* algorithm. This method introduces an expected cost evaluation function to achieve weighted fusion of path costs and heuristic values from multiple maps, allowing the algorithm to integrate obstacle distributions and weight information across different environments. A random obstacle replacement mechanism is further designed to maintain path feasibility by locally substituting blocked nodes with adjacent accessible nodes, thereby ensuring continuity without global replanning. Through the combination of multi-map information fusion and local obstacle handling, the algorithm generates a globally optimized path that balances planning efficiency, robustness, and adaptability in uncertain rescue scenarios. Experiment results for a 50 × 50 map scenario show that the improved algorithm significantly outperforms single-map planning results in terms of path redundancy, total length, and turning characteristics. The expansion experiments demonstrate that the paths planned by the proposed algorithm are highly consistent with the optimal paths in terms of direction and local deviations, verifying its good feasibility and effectiveness.Full article

Overhead Rigid Conductor Systems (ORCS) are widely used in modern urban rail networks, where precise monitoring of contact wire geometry is critical for safe operation. Traditionally, these critical parameters have been primarily obtained through expensive and environmentally sensitive industrial camera systems, presenting significant [...] Read more.

Overhead Rigid Conductor Systems (ORCS) are widely used in modern urban rail networks, where precise monitoring of contact wire geometry is critical for safe operation. Traditionally, these critical parameters have been primarily obtained through expensive and environmentally sensitive industrial camera systems, presenting significant limitations. This work presents a novel framework for predicting dynamic stagger and localizating section overlaps within ORCS, offering a more cost-effective and robust alternative. The methodology integrates three components: a beam-based model to obtain dynamic stagger under moving-load conditions, a difference matrix representation with kurtosis-guided lag selection and prominence-informed peak detection for overlap localization, and zero-phase Butterworth filtering to suppress dynamic pulsations. The framework was validated on 32 distinct overlap segments across both triangular and sinusoidal ORCS geometries. The section overlap classifier achieved an$F_{\beta }$-score of 1 for both layout types, indicating 100% identification of overlaps. Furthermore, the framework exhibits excellent prediction of the stagger probability distribution, with Bhattacharyya distances between measured and predicted distributions of 0.0115 for triangular layouts and 0.0517 for sinusoidal layouts. The section-wise mean Bhattacharyya distance was validated as 0.0734, and the framework maintained robustness across ±10% speed fluctuations. This research provides a reliable, robust, and economically viable method for ORCS dynamic stagger monitoring.Full article

Multi-robot systems, owing to their parallel operation and cooperative capabilities, have become an important means of improving the efficiency of complex workpiece machining. However, task allocation methods directly determine the overall system performance, which is particularly critical in scenarios with high curvature and [...] Read more.

Multi-robot systems, owing to their parallel operation and cooperative capabilities, have become an important means of improving the efficiency of complex workpiece machining. However, task allocation methods directly determine the overall system performance, which is particularly critical in scenarios with high curvature and stringent stiffness requirements. This study focuses on a Dual-Robot Carving System (DRCS) and proposes a task allocation method that incorporates stiffness performance constraints, using stereoscopic stone carving as a representative application. First, a workstation optimization model is developed based on the average normal stiffness as the evaluation metric, enabling the selection and allocation of high-complexity tasks. This approach not only ensures machining stiffness but also effectively decouples the task allocation problem. Subsequently, two allocation strategies are designed for low-complexity tasks: one based on machinability and the other on machining time balancing. Comparative simulations and physical experiments are conducted to evaluate the efficiency differences between the proposed methods and the single-robot machining mode. The results show that the machining time balancing strategy improves efficiency by 14.33% compared with the machinability-based strategy, and by 84.78% compared with the single-robot mode. These findings demonstrate the effectiveness of the proposed method in enhancing dual-robot collaborative efficiency and provide a novel modeling perspective and technical support for multi-robot task allocation under stiffness constraints in complex workpiece machining.Full article

Traditional fault diagnosis methods, which rely on single-vibration signals, are insufficient for capturing the complexity of mechanical systems. As neural networks evolve, attention mechanisms often fail to preserve local features, which can reduce diagnostic accuracy. Additionally, transfer learning using single-domain metrics struggles under [...] Read more.

Traditional fault diagnosis methods, which rely on single-vibration signals, are insufficient for capturing the complexity of mechanical systems. As neural networks evolve, attention mechanisms often fail to preserve local features, which can reduce diagnostic accuracy. Additionally, transfer learning using single-domain metrics struggles under fluctuating conditions. To address these challenges, this paper proposes an innovative adversarial training approach based on the Time–Frequency Fused Vision Transformer Network (TFFViTN). This method processes signals in both the time and frequency domains and incorporates a robust attention mechanism, along with a novel metric that combines Wasserstein distance and maximum mean discrepancy (MMD) to precisely align feature distributions. Adversarial training further strengthens domain-invariant feature extraction. Experiments on bearing and gear datasets demonstrate that our model significantly improves diagnostic performance, stability, and generalization.Full article

Bearing health monitoring is essential for ensuring the reliability and operational safety of induction machines, as bearing faults remain among the most frequent failure modes in rotating electrical equipment. This work contributes to condition monitoring by enhancing the robustness of health indicators and [...] Read more.

Bearing health monitoring is essential for ensuring the reliability and operational safety of induction machines, as bearing faults remain among the most frequent failure modes in rotating electrical equipment. This work contributes to condition monitoring by enhancing the robustness of health indicators and developing a supply-frequency-independent health indicator (HI) for bearing fault diagnosis using Motor Current Signature Analysis (MCSA). The objective is to design an HI capable of reliably representing the bearing degradation state under varying operating conditions, particularly when the supply frequency changes. To achieve this, the study briefly examines the key physical mechanisms governing the detectability of bearing-related spectral signatures—including rotational frequency, unbalanced magnetic pull, eddy currents, skin effect, and hydrodynamic forces. The theoretical analysis establishes the overall trend expected under varying supply frequencies and clarifies how these phenomena collectively influence the spectral characteristics of the fault components and the frequency-dependent evolution of their amplitudes. These insights are experimentally validated using induction machines fitted with bearings of two fault severities. Leveraging this physical understanding, a modified regression-based compensation model is introduced to reduce the frequency-dependent variation in the HI. The resulting compensating factor effectively stabilizes the frequency response, producing a more consistent and monotonic degradation trend across the tested conditions. The proposed method is computationally lightweight, does not require run-to-failure data or detailed physical modeling, and is suitable for real-time implementation. By integrating physical insight with data-driven modeling, this work presents a practical and frequency-independent HI framework that can be readily deployed within digital-twin-based condition monitoring architectures for induction machines.Full article

Biped robots based on parallel mechanisms hold great potential for applications in complex terrains. Based on a 4-UPU parallel mechanism, this paper proposes a novel biped robot that achieves alternating bipedal locomotion and turning with only six actuators by employing fixed/moving platform switching [...] Read more.

Biped robots based on parallel mechanisms hold great potential for applications in complex terrains. Based on a 4-UPU parallel mechanism, this paper proposes a novel biped robot that achieves alternating bipedal locomotion and turning with only six actuators by employing fixed/moving platform switching and following an “upper platform + lower foot” continuous gait strategy. Using the influence coefficient method, the first order and second order kinematic influence coefficient matrices of the biped robot were derived. Based on the principle of virtual work, a dynamic model of the robot was formulated, and its validity was verified through numerical simulations. The dynamic performance of the robot was further evaluated using the Dynamic Manipulability Ellipsoid (DME) index, while its stability during step-climbing and turning was analyzed using the Zero-Moment Point (ZMP) method. The results demonstrate that the dual-platform biped robot features a rational structure and exhibits robust stability during step-climbing and turning.Full article

Among gearbox faults, gear tooth faults are dominant. Although the traditional vibration spectrum analysis method is the mainstream diagnostic method, it has limitations such as sensitivity to environmental noise and high sensor deployment cost. Based on the influence of the meshing stiffness of [...] Read more.

Among gearbox faults, gear tooth faults are dominant. Although the traditional vibration spectrum analysis method is the mainstream diagnostic method, it has limitations such as sensitivity to environmental noise and high sensor deployment cost. Based on the influence of the meshing stiffness of the faulty gear on the dynamic transmission error of the gear, this study innovatively proposes to use the transmission error to diagnose and identify typical gear tooth faults. This paper first calculates the time-varying stiffness of typical faulty gear teeth based on the potential energy method, and analyzes the influence of various faults and environmental noise on the dynamic transmission error signal and vibration signal by establishing a six-degree-of-freedom gear transmission dynamics model. Then, a gear transmission experimental platform is built to synchronously collect the vibration acceleration and transmission error data of the gearbox. The convolutional neural network is used to classify the data under different sample lengths and different noise intensities. The results show that the transmission error signal under the same conditions has a higher gear fault diagnosis accuracy. The proposed method can not only improve the accuracy and anti-interference of gear fault diagnosis but also reduce the deployment cost of signal acquisition, providing a new paradigm for gear condition monitoring.Full article

To mitigate the inadequate performance of traditional hydraulic systems, mechanical feedback-based digital hydraulic technology is applied to a 3-degree-of-freedom (3-DOF) crane. Digital hydraulic cylinders drive the pitch mechanism, and digital hydraulic motors power the rotary and winch mechanisms. By analyzing the working principles [...] Read more.

To mitigate the inadequate performance of traditional hydraulic systems, mechanical feedback-based digital hydraulic technology is applied to a 3-degree-of-freedom (3-DOF) crane. Digital hydraulic cylinders drive the pitch mechanism, and digital hydraulic motors power the rotary and winch mechanisms. By analyzing the working principles of digital hydraulic cylinders and motors, transfer functions of the 3-DOF actuators are derived. AMESim simulation models are established for each actuator, with model validity verified. Based on these models, simulation analysis of the digital hydraulic system is performed to examine key influencing factors: motor speed, motor subdivision, system flow rate, digital valve opening, and throttle groove shape. System characteristics are obtained, and corresponding optimization schemes are proposed. After optimization, the comprehensive performance of the digital hydraulic system is improved by 1.29%. This study provides theoretical support for the engineering application of digital hydraulic systems in cranes, clarifies their operational specifications and optimization pathways, and exhibits substantial engineering application value.Full article

Problems such as unreasonable processability or model defects generated in the design stage will lead to continuous rework during the manufacturing process, which greatly increases the manufacturing cost of the product. Through manufacturability analysis, the process designer can find design defects that are [...] Read more.

Problems such as unreasonable processability or model defects generated in the design stage will lead to continuous rework during the manufacturing process, which greatly increases the manufacturing cost of the product. Through manufacturability analysis, the process designer can find design defects that are difficult to manufacture, impossible to manufacture, or have high manufacturing costs as early as possible, so as to reduce the number of round trips between design and process, and shorten the product development cycle. However, it is difficult for the current rule-based manufacturability analysis method to obtain professional knowledge and construct a complete manufacturability analysis rule repository. Therefore, a manufacturability analysis method based on a graph neural network is proposed. First, the attribute adjacency graph and UV gridding are combined to characterize the part model data, which can effectively represent the topological information and geometric information on the part model. At the same time, parameter information on the spherical coordinate system is used to ensure rotation and translation invariance; then, based on the graph representation of the part model, a hierarchical graph neural network is constructed, which is divided into three levels, edge, node, and graph, for encoding, information transmission and updating, and expanding the receptive field for better node classification to support manufacturability analysis. Finally, graph contrastive learning is used as a regularization technique in the pre-training stage to maximize the similarity of graph representations between different views to improve prediction performance. Manufacturability analysis tests were carried out on the constructed part model dataset, and the experimental results showed that the method has good performance.Full article

Achieving high operational efficiency in modern manufacturing requires the seamless integration of production scheduling and intralogistics coordination. However, in flexible assembly shops, the decoupling between production sequencing and automated guided vehicle (AGV) routing often leads to resource conflicts, unbalanced workloads, and inefficient energy [...] Read more.

Achieving high operational efficiency in modern manufacturing requires the seamless integration of production scheduling and intralogistics coordination. However, in flexible assembly shops, the decoupling between production sequencing and automated guided vehicle (AGV) routing often leads to resource conflicts, unbalanced workloads, and inefficient energy utilization. To address this challenge, this study proposes an improved genetic algorithm (IGA) for integrated production–logistics scheduling. The innovation lies in a triple-chain encoding strategy that concurrently represents production, transportation, and time-window constraints, coupled with adaptive crossover and mutation operators for enhanced population diversity. Furthermore, a time-window-constrained Dijkstra routing mechanism is incorporated to prevent AGV conflicts and improve synchronization between machines and logistics. Two representative shop-floor scenarios—baseline and disturbed conditions—were designed for validation. Comparative experiments against a standard genetic algorithm (GA) and a two-stage heuristic demonstrate that the IGA achieves 9.5% and 6.7% reductions in average makespan, respectively, while maintaining less than 1% deviation under 10% random disturbances. Statistical tests (p < 0.01, Cohen’s d > 1.4) confirm the method’s robustness and practical effectiveness. The proposed approach provides a reliable and implementable optimization framework that enhances coordination between production and AGV systems in flexible assembly environments and offers a practical reference for smart manufacturing scheduling and digital twin applications.Full article

Fuel consumption is a critical concern for both environmental and economic reasons. This study proposes a method to operate an engine near the minimum fuel consumption curve by adjusting gear ratios. Using a one-dimensional backward vehicle simulation model, the actual fuel consumption is [...] Read more.

Fuel consumption is a critical concern for both environmental and economic reasons. This study proposes a method to operate an engine near the minimum fuel consumption curve by adjusting gear ratios. Using a one-dimensional backward vehicle simulation model, the actual fuel consumption is controlled to follow the desired fuel consumption derived from the Honda Insight fuel conversion map through a proportional–integral–derivative-based approach. Actual fuel consumption, which comes from the driving cycle, and desired consumption, which comes from minimum fuel consumption, are used to determine the optimal gear ratio by supposing that the manual transmission Honda becomes a continuous variable transmission and that gear ratio is a control input for tracking between actual and desired fuel consumption. The minimum fuel consumption curve is obtained from the intersection of constant power and fuel mass flow rate curves. The accuracy of tracking between desired and actual fuel consumption is evaluated across standard driving cycles (New European Driving Cycle, worldwide harmonized light vehicle test procedure Class-3, highway fuel economy test driving, federal test procedure), yielding R² values of 0.94, 0.91, 0.88, and 0.91, respectively. These results demonstrate that variable gear ratios can enable operation close to the minimum fuel consumption curve under real driving conditions by considering gearbox dimension limits.Full article

Torsional vibrations in rotating machinery cause mechanical wear, electronic malfunctions, and a reduction in service life, particularly in high-speed industrial systems such as rotors. This study presents the development and integration of a Tuned Mass Damper (TMD) designed to mitigate damage to a [...] Read more.

Torsional vibrations in rotating machinery cause mechanical wear, electronic malfunctions, and a reduction in service life, particularly in high-speed industrial systems such as rotors. This study presents the development and integration of a Tuned Mass Damper (TMD) designed to mitigate damage to a die-cutting system. A theoretical model is formulated, demonstrating how an auxiliary mass coupled to a rotor absorbs energy at a designated frequency. Frequency response function analysis identifies torsional resonances, which are validated through a multibody model providing modal shapes and overall dynamic behavior. The design is carried out in strict compliance with the constraints and limitations of a real packaging machine. The TMD employs anti-vibration mounts, selected and tuned to deliver a required torsional stiffness based on finite element analysis used to determine their optimal radial placement. Experimental testing confirms theoretical predictions: the added inertia significantly reduced the first resonance peak and attenuated rotary torque oscillations, thereby improving the system’s dynamic response. These findings highlight passive torsional damping as a robust and effective approach to improving the rotor’s dynamic response and reducing alternating stresses, which predictively contributes to enhanced operational reliability and reduced machine downtime.Full article

Currently, fifth-generation (5G) communication has emerged as the most promising candidate for next-generation railway-dedicated communication systems. Condition monitoring of 5G networks is critical for ensuring the continuity and reliability of train–ground communications. In this paper, a real-time monitoring technology is proposed, which is [...] Read more.

Currently, fifth-generation (5G) communication has emerged as the most promising candidate for next-generation railway-dedicated communication systems. Condition monitoring of 5G networks is critical for ensuring the continuity and reliability of train–ground communications. In this paper, a real-time monitoring technology is proposed, which is based on generalized channel characteristics extracted from received Demodulation Reference Signals (DM-RSs). Furthermore, a corresponding monitoring system has been developed based on the Radio Frequency System on Chip (RFSoC). Experimental results demonstrate that the proposed condition monitoring system exhibits excellent performance: it can accurately measure key network metrics (including field strength, multipath components, and frequency offset) and enable real-time monitoring of the operational condition of 5G radio access networks (RAN) and on-board terminals. Future work will focus on integrating the monitoring system into on-board terminals.Full article