Search Results (78)

Optical tactile sensing is gaining traction as a foundational technology in collaborative and human-interactive robotics, where reliable touch and pressure feedback are critical. Traditional systems based on total internal reflection (TIR) and frustrated TIR (FTIR) often require complex infrared setups and lack adaptability to curved or flexible surfaces. To overcome these limitations, we developed OptoSkin—a novel tactile platform leveraging direct time-of-flight (ToF) LiDAR principles for robust contact and pressure detection. In this extended study, we systematically evaluate how key optical properties of waveguide materials affect ToF signal behavior and sensing fidelity. We examine a diverse set of materials, characterized by varying light transmission (82–92)%, scattering coefficients (0.02–1.1) cm⁻¹, diffuse reflectance (0.17–7.40)%, and refractive indices 1.398–1.537 at the ToF emitter wavelength of 940 nm. Through systematic evaluation, we demonstrate that controlled light scattering within the material significantly enhances ToF signal quality for both direct touch and near-proximity sensing. These findings underscore the critical role of material selection in designing efficient, low-cost, and geometry-independent optical tactile systems. Full article

This study develops a real-time energy-efficient control strategy for distributed-drive electric tractors (DDETs) to minimize electrical energy consumption during traction operations. Taking a four-wheel independently driven DDET as the research object, we conduct dynamic analysis of draft operations and establish dynamic models of individual components in the tractor’s drive and transmission system. A backpropagation (BP) neural network-based operational speed prediction model is constructed to forecast operational speed within a finite prediction horizon. Within the model predictive control (MPC) framework, a real-time energy-efficient control strategy is formulated, employing a dynamic programming algorithm for receding horizon optimization of energy consumption minimization. Through plowing operation simulation with comparative analysis against a conventional equal torque distribution strategy, the results indicate that the proposed real-time energy-efficient control strategy exhibits superior performance across all evaluation metrics, providing valuable technical guidance for future research on energy-efficient control strategies in agricultural electric vehicles. Full article

The torsional stiffness of rehabilitation robot joints is a critical performance determinant, significantly affecting motion accuracy, stability, and user comfort. This paper introduces an innovative traction drive mechanism that transmits torque through friction forces, overcoming mechanical impact issues of traditional gear transmissions, though accurately modeling surface roughness effects remains challenging. Based on fractal theory, this study presents a comprehensive torsional stiffness analysis for advanced traction drive joints. Surface topography is characterized using the Weierstrass–Mandelbrot function, and a contact mechanics model accounting for elastic–plastic deformation of micro-asperities is developed to derive the tangential stiffness of individual contact pairs. Static force analysis determines load distribution, and overall joint torsional stiffness is calculated through the integration of individual contact contributions. Parametric analyses reveal that contact stiffness increases with normal load, contact length, and radius, while decreasing with the tangential load and roughness parameter. Stiffness exhibits a non-monotonic relationship with fractal dimension, reaching a maximum at intermediate values. Overall system stiffness demonstrates similar parameter dependencies, with a slight decrease under increasing output load when sufficient preload is applied. This fractal-based model enables more accurate stiffness prediction and offers valuable theoretical guidance for design optimization and performance improvement in rehabilitation robot joints. Full article

A continuously variable transmission can improve the energy efficiency of actuators with rotary output by providing an optimum transmission ratio. A continuously variable transmission based on circumferentially arranged disks (CAD CVT) is a new type of CVT that is highly beneficial for applications requiring large torques, like heavy road transport. However, its major drawback is that its efficiency drops in the low torque region. To overcome this problem, the current paper proposes an improved mechanical design in which the force on traction disks is changed according to the instantaneous torque requirement, thus resulting in improved efficiency in low torque regions. Furthermore, a hydraulic-actuation-based control system has been designed to ensure the optimum control of the improved mechanical design. The improved mechanical design of the CAD CVT is named CAD CVT-II, which is highly beneficial for variable torque applications such as road transport and wind turbines. Full article

In the electric locomotive traction transmission system, a four-quadrant rectifier has a high fault rate owing to the complicated control and bad operating conditions, and the fault directly affects the system’s safety and stability. To address such an issue, a rectifier fault diagnosis approach regarding a local tangent space alignment (LTSA) dimensionality reduction to optimize the high-dimensional energy entropy feature is proposed. Firstly, the fault signal is analyzed by using different wavelet functions through wavelet packet multi-resolution decomposition technology so as to extract the frequency band information of the signal. Each wavelet function corresponds to a specific frequency band; the energy–information entropy ratio of each frequency band coefficient is calculated, and then, the wavelet function and optimal frequency band, which are appropriate for the fault signal, are determined. Secondly, the energy entropy of each coefficient in the optimal frequency band is calculated to form the high-dimensional energy entropy feature. The LTSA algorithm is adopted to optimize the high-dimensional feature, through the fault sample number and clustering results, solve the difficulty of selecting the inherent dimension and nearest neighbor number in high-dimensional data, and obtain the simple and effective low-dimensional feature vector to describe the fault features, which reduces the conflict and redundancy between features. Finally, the optimized fault features are used as an input to the classifier support vector machine (SVM), and the fault types are obtained through training and testing. To validate the efficacy of the presented approach, it is tested from the aspects of noise environment, sample proportion and algorithm complexity, and compared with advanced methods. The results indicate that the proposed technique attains an average accuracy of 99.0625% in four-quadrant rectifier fault diagnosis. Under a different signal-to-noise ratio (SNR) and different training and test ratios, the average value after 30 diagnoses is better. Compared with other methods, this method shows a high diagnostic rate and strong robustness in terms of output voltage, noise, training and test ratio. Full article

The Vehicular Edge-Cloud Computing (VECC) paradigm has gained traction as a promising solution to mitigate the computational constraints through offloading resource-intensive tasks to distributed edge and cloud networks. However, conventional computation offloading mechanisms frequently induce network congestion and service delays, stemming from uneven workload distribution across spatial Roadside Units (RSUs). Moreover, ensuring data security and optimizing energy usage within this framework remain significant challenges. To this end, this study introduces a deep reinforcement learning-enabled computation offloading framework for multi-tier VECC networks. First, a dynamic load-balancing algorithm is developed to optimize the balance among RSUs, incorporating real-time analysis of heterogeneous network parameters, including RSU computational load, channel capacity, and proximity-based latency. Additionally, to alleviate congestion in static RSU deployments, the framework proposes deploying UAVs in high-density zones, dynamically augmenting both storage and processing resources. Moreover, an Advanced Encryption Standard (AES)-based mechanism, secured with dynamic one-time encryption key generation, is implemented to fortify data confidentiality during transmissions. Further, a context-aware edge caching strategy is implemented to preemptively store processed tasks, reducing redundant computations and associated energy overheads. Subsequently, a mixed-integer optimization model is formulated that simultaneously minimizes energy consumption and guarantees latency constraint. Given the combinatorial complexity of large-scale vehicular networks, an equivalent reinforcement learning form is given. Then a deep learning-based algorithm is designed to learn close-optimal offloading solutions under dynamic conditions. Empirical evaluations demonstrate that the proposed framework significantly outperforms existing benchmark techniques in terms of energy savings. These results underscore the framework’s efficacy in advancing sustainable, secure, and scalable intelligent transportation systems. Full article

In the pursuit of sustainable urban transportation, electric buses (EBs) have emerged as a promising solution to reduce emissions. The increasing adoption of EBs highlights the critical need for accurate energy consumption prediction. This study presents a comprehensive methodology integrating traction modeling with a Light Gradient Boosting Machine (LightGBM)-based trip-level energy consumption prediction framework to address challenges in power system efficiency and passenger load estimation. The proposed approach combines transmission system efficiency evaluation with dynamic passenger load estimation, incorporating temporal, weather, and driving pattern features. The LightGBM model, hyperparameter tuned through Bayesian Optimization (BO), achieved a mean absolute percentage error (MAPE) of 3.92% and root mean square error (RMSE) of 1.398 kWh, outperforming traditional methods. SHAP analysis revealed crucial feature impacts on trip-level energy consumption predictions, providing valuable insights for operational optimization. The model’s computational efficiency makes it suitable for real-time IoT applications while establishing precise parameters for future optimization strategies, contributing to more sustainable urban transit systems. Full article

Permanent magnetic linear synchronous motors (PMLSMs) have emerged as a promising solution for low-carbon urban rail transit systems due to their superior energy efficiency. However, their widespread adoption is hindered by significant challenges in achieving high-precision cooperative control and fault-tolerant operation across multi-PMLSMs. To address these issues, this paper proposed a novel composite observer-based adaptive fault-tolerant cooperative control framework, which enables reliable speed synchronization in multi-PMLSM urban rail traction systems through three key innovations. Initially, the stuck fault of the actuator is modeled based on the PMLSM dynamic model, and a composite observer is proposed to estimate lumped disturbances and actuator faults simultaneously, enhancing the system’s robustness against uncertainties and faults. A novel sliding mode control scheme with adaptive parameters is subsequently developed to compensate for disturbances and improve tracking accuracy. Furthermore, two event-triggered schemes are devised to reduce the communication burden, ensuring efficient data transmission without compromising control performance. The proposed method ensures high-precision synchronization and fault tolerance under actuator stuck faults, bias faults, and external disturbances, as validated by simulation results. By improving energy efficiency and reducing communication load, the proposed method contributes to the development of low-carbon urban rail transit systems, aligning with global sustainability goals. Full article

Aiming at the problem that the main circuit grounding fault in the traction drive system of locomotives and high-speed trains can only be diagnosed under a single operating condition and cannot be warned about early, a mechanism and data-driven real-time evaluation and full operating condition fault warning method for ground insulation degradation is proposed. Firstly, based on the mechanism of grounding faults, the circuit characteristics of the main circuit of the traction transmission system under different grounding fault conditions are analyzed, and mathematical models are established for the detection of various grounding faults and sensor signals under different operating conditions, as well as for evaluating the degree of degradation of grounding faults. Secondly, based on engineering application experience, a feature index set that can accurately classify different types of grounding faults is extracted. Combined with on-site fault case data, a decision tree method is used to establish a classification model between the feature index set and typical grounding fault sources under different operating conditions, which is then converted into a fault diagnosis rule library. Finally, real-time collection of relevant sensor signals, based on the fault diagnosis rule library and the degradation degree evaluation model of grounding faults, enables real-time detection and warning of grounding faults under all operating conditions to ensure train safety and provide key information support for optimal degraded operation in the future. The test result based on controller hardware in the loop shows that the method proposed in this paper can achieve accurate detection and localization of grounding faults under different operating conditions and can provide real-time warning of the severity of grounding faults, which has good engineering application value. Full article

In light of the issues associated with the laying process of transmission line cables, including concealed security risks and contact collisions between pulleys and cables, which primarily stem from reliance on drawings, this paper introduces a simulation methodology for the cable laying construction process utilizing Building Information Modeling (BIM) technology. Initially, two-dimensional DWG graphic data are employed to develop a model of the target equipment and construction environment using BIM software (Solid works 2020). Subsequently, the cable is accurately modeled by applying ADAMS virtual prototype technology, the bushing force connection method, and the macro command language. This allows for the construction of a three-dimensional real cable laying system for transmission lines, enabling the simulation of the dynamic cable laying process in the field. Subsequently, an error analysis is conducted to compare the axial tension and laying speed of the cable with theoretical calculation values. The study then proceeds to analyze tension fluctuations during the cable laying process and assess the load-bearing capacity of the pulleys, thus facilitating effective control of the construction process and enhancing safety measures. The findings indicate that the proposed method can accurately and efficiently simulate the on-site cable laying construction process, with numerical errors maintained below 5%, thereby validating the integrity of the model. Furthermore, the traction overload safety protection amplification coefficient is determined to be α = 1.5. It is highlighted that the bearing capacity of the block must exceed 60% of the load carried by the conductor at constant speed. This research provides a theoretical foundation for addressing safety hazards in cable laying engineering and holds certain engineering value. Full article

The process of establishing relay protection and automation (RPA) settings for electric power systems (EPSs) entails complex calculations of operating modes. Traditionally, these calculations are based on symmetrical components, which require the building of equivalent circuits of various sequences. This approach can lead to errors both when identifying the operating modes and when modeling the RPA devices. Proper modeling of measuring transformers (MTs), symmetrical component filters (SCFs), and circuits connected to them effectively solves this problem, enabling the configuration of relay protection and automation systems. The methods of modeling the EPS in phase coordinates are proposed to simultaneously determine the operating modes of high-voltage networks and secondary circuits connected to the current and voltage transformers. The MT and SCF models are developed to concurrently identify the operating modes of secondary wiring circuits and calculate the power flow in the controlled EPS segments. This method is effective in addressing practical problems related to the configuration of the relay protection and automation systems. It can also be used when establishing cyber–physical power systems. For a comprehensive check of the adequacy of the MT models, 140 modes of the electric power system were determined which corresponded to time-varying traction loads. Based on the results of calculating the complexes of currents and voltages at the MT terminals, parametric identification of the power transmission line was performed. Based on this, the model of this transmission line was adjusted; repeated modeling was carried out, and errors were calculated. The modeling results showed a high accuracy when calculating the modules and phases of voltages using the identified model. The average error value for current modules was 0.6%, and for angles, it was 0.26°. Full article

Pantograph-based electrical current transmission systems are used in electric traction vehicles. The contact surface between the pantograph and the catenary wire experiences mechanical and thermal effects during the train’s movement. Typically, this contact surface on the pantograph is covered by a segmented carbon or copper rod, attached to an aluminum base. Railways implement organizational measures for pantograph condition monitoring, based on scheduled inspections. Constitutionally, the option to replace contact elements or individual segments of the pantograph exists if wear is detected. Many scientific publications describe ideas for pantograph visualization and automated condition monitoring. These ideas are based on analyzing mechanical vibrations generated by the pantograph, acoustic vibration signal analysis, 3D geometric data of the pantograph surface captured by laser scanning, and combinations of several methods. However, in these publications, mechanical vibration analysis is limited to signal shape and spectral analysis. The possibility of treating the vibration signal as a random process using statistical methods has not been utilized. This study describes the possibility of evaluating classified mechanical pantograph vibrations using the signal’s autocorrelation transformation. A laboratory experiment confirmed the proposed method for evaluating informative signal classification features. The proposed method can distinguish between signals generated by a defective pantograph surface and identify different types of defects. Full article

This paper investigates the application of high-capacity supercapacitors in railway systems, with a particular focus on their role in energy recovery during braking processes. The study highlights the potential for significant energy savings by capturing and storing energy generated through electrodynamic braking. Experimental measurements conducted on a diesel–electric multiple unit revealed that approximately 28.3% to 30.5% of the energy could be recovered from the traction network, regardless of the type of drive used—whether electric or diesel. This research also explores the integration of starch-based carbon as an electrode material in supercapacitors, offering an innovative, sustainable alternative to traditional graphite or graphene electrodes. The carbon material was obtained through a simple carbonization process, with experimental results demonstrating a material capacity of approximately 130 F/g. To quantify the energy recovery, calculations were made regarding the mass and power requirements of the supercapacitors. For the tested vehicle, it was estimated that around 28.7% of the energy could be recovered during the braking process. To store 15 kWh of energy, the total mass of the capacitors required is approximately 245.1 kg. The study emphasizes the importance of increasing voltage levels in railway systems, which can enhance energy transmission and utilization efficiency. Additionally, the paper discusses the necessity of controlled energy discharge, allowing for the flexible management of energy release to meet the varying power demands of trains. By integrating high-voltage supercapacitors and advanced materials like starch-based carbon, this research paves the way for more sustainable and efficient railway systems, contributing to the industry’s goals of reducing emissions and improving operational performance. The findings underscore the crucial role of these capacitors in modernizing railway infrastructure and promoting environmentally responsible transportation solutions. Full article

As a crucial step in food production, tillage and land preparation play a pivotal role in achieving sustainable crop production and improving the soil environment. However, accurate assessment of the load that agricultural machinery implements during the operation process has always been a vexing problem that needs urgent solutions. In this paper, an extrapolation and reconstruction framework for the time-domain load is constructed based on the probability-weighted moments (PWM) estimation and the peaks-over-threshold function, and the load spectrum is obtained for agriculture general power machinery. Firstly, the load acquisition system was developed, the traction resistance and output torque of the tractor were measured, and the collected load signals were preprocessed. Next, the mean excess function and PWM estimation are introduced to select the optimal threshold and generalized Pareto distribution (GPD) fitting parameters and the extreme load distribution that exceeds the threshold range is fitted. The extreme points in the original data are replaced by generating new extreme points that follow the GPD distribution, and the extrapolation of the load spectrum is achieved. Finally, the real extrapolated load spectrum was validated based on statistical characteristics and rainflow counting analysis, and the correlation coefficient between the fitting data and the extreme load samples was greater than 0.99. It can retain the load sequence characteristics of the original load to a great extent, truly reflecting the load state during the operation of agricultural machinery. Meanwhile, the characteristics of the load spectrum can be accurately obtained, such as extreme, mean, and amplitude values, and the real load during deep loosening and rotary tillage are accurately described. The values provide more authentic and reliable data support for the subsequent selection of optimal operating parameters, reliability design of the power transmission system, and the life assessment of the agricultural implements. Full article

Trends in the use of renewable energy sources to power buildings do not bypass objects for which maintaining a power supply is critical. This also applies to railway signal boxes. The aim of the research work was to test the multisource power supply system for a railway signal box with power electronic converter systems and a DC bus, built as part of the research project. The assumption for powering the railway signal box building was to use renewable sources, energy storage devices, and a 3 kV DC traction network as the second required power supply grid. Both power grids were connected by power electronic converters, and the power values of the converters were set based on the calculated power balance values using the values measured at the system nodes and the set constraints. The tests primarily tested the response of the power supply system to changes in load power and power generated by the photovoltaic source, as well as the charge level of the energy storage devices. The correctness of the control algorithm’s operation was assessed based on the recorded power values in the power supply system nodes. The tests were carried out for 60 scenarios that covered all normal and emergency operating conditions. During the tests, delays in response to changes in the power supplied to the converters and the values of circular power flow between the power grid connections were recorded. The recorded delays ranged from 2 to about 50 s and the circular power flows did not exceed 1500 W. Based on the results of the tests, it was found necessary to improve the power measurement system in the power supply system nodes and to improve the quality of communication and the transmission time of measurement data transmission time. Full article