Accurate in-motion inspection of railway wheels requires reliable estimation of wheel diameter, flange geometry, and localized tread spalling under partial views, noise, and surface wear. Many existing systems either infer wheel condition indirectly, measure only a subset of parameters, or suffer from ambiguous alignment on nearly axis-symmetric wheel surfaces and biased diameter fitting when defect points are included. These issues limit practical wayside deployment where observations are sparse and partially occluded. This paper presents a stereo-vision sensing system and an integrated 3D measurement pipeline for high-precision wheel condition assessment. The proposed approach is novel, to our knowledge, in combining keypoint-constrained congruent-set registration to resolve near-symmetry ambiguity with rim-corner plane-circle fitting to suppress wear/defect-induced bias in rolling-radius estimation. Coarse alignment is obtained via constrained congruent-set sampling and refined by least-squares pose optimization to fuse sparse observations into a consistent wheel model. Wheel diameter is estimated from rim-corner points, while flange height and thickness are extracted using projection-based interval sampling and NURBS profile fitting. Spalling regions are segmented by boundary extraction and clustering, and spalling length and depth are quantified through cylindrical projection. Experiments validated against gauge-based ground truth showed mean absolute errors of 0.08 mm (rolling radius), 0.05 mm (flange height), 0.06 mm (flange thickness), 0.02 mm (spalling length), and 0.09 mm (spalling depth), and reduced rolling-radius error by 38.5% compared with a conventional baseline. The results demonstrate a practical, submillimeter solution for in-motion wheel condition monitoring.

Wheels are critical components of railway vehicles, and the dynamic measurement of wheel parameters is of paramount importance for the safe operation of trains.To enhance the matching accuracy in the existing dynamic measurement processes for train wheel parameters, this paper proposes an improved point cloud registration algorithm based on key point fusion of the Super Four-Points Congruent Sets (Super-4PCS) and Iterative Closest Point (ICP) algorithm. Firstly, point cloud filtering and normal estimation are performed on the wheel point cloud data to obtain source and target point clouds with normal information. Subsequently, the Intrinsic Shape Signatures (ISS) algorithm is employed to extract key points, and the Fast Point Feature Histograms (FPFH) point cloud feature descriptor is utilized to characterize the extracted key points. Then, a two-level registration strategy is used to improve registration accuracy, in which the Super-4PCS algorithm is applied for primary coarse registration and the ICP algorithm is used for the secondary fine registration, respectively. Finally, the experiment is conducted to validate the proposed algorithm and the performance of the algorithm is further comparative analyzed through the listed registration evaluation metrics. Experimental results demonstrate that the proposed algorithm significantly improves registration accuracy and robustness for wheel point cloud data, with the Root Mean Square Error (RMSE) reduced from 0.0631 to 0.0002, and the Mean Absolute Error (MAE) reduced from 0.0671 to 0.00026, compared to traditional algorithms. However, the algorithm’s performance is sensitive to point cloud density and noise levels, and its effectiveness may vary under different environmental conditions.

The structural damage of rail vehicle bogie frames is mostly caused by fatigue cumulative damage under the action of multi-level complex loads. Under complex loads, the sequence effect and coupling effect between loads have a significant impact on structural fatigue damage. Meanwhile, the performance degradation of components during service also affects the damage accumulation and the remaining life of the structure. Traditional linear cumulative damage models and the Manson-Halford model ignore the influences of load sequence effect, load interaction, and structural degradation on fatigue life, leading to inaccurate remaining life predictions. By introducing degradation parameters to modify the Manson-Halford model, an improved nonlinear cumulative damage model is proposed, and its accuracy is verified by combining standard specimen fatigue test data. Finally, the improved cumulative damage model was applied to engineering practice in engineering to predict and evaluate the fatigue life of bogie frames.

The recent rapid development of the computer‐aided design technologies has provided concrete support for the education and research in the field of railway transportation. Virtual reality (VR) technology is becoming particularly important in the field of experimental teaching due to its low cost, low restriction and immersion. In this work, we have designed a virtual simulation untried system of the electric joint control in electric locomotives based on VR technology. Based on the user's understanding of the three circuit principles in the experiment, together with the recognition and operation ability to control appliances and equipment, the system assessment system has been scored and evaluated accordingly. The tests of the virtual simulation system have shown the experimental teaching can reduce the cost while effectively improving the learning effect.

In this paper, Fe-based and Co-based alloy powders were chosen to perform laser cladding on wheel materials through conventional laser cladding (CLC) and ultra-high-speed laser cladding (UHSLC) processes, respectively. The microstructures, element distribution, phase composition and hardness of the Fe-based alloy and Co-based alloy coating layers using the CLC and UHSLC processes were compared and analysed. The results show that the CLC and UHSLC alloy coatings were dense and free of defects such as pores and cracks. Compared with the CLC alloy coating, the grain size of the UHSLC alloy coating was smaller, the coating composition was close to the powder design composition, and the distribution of Cr within and between the grains was more uniform. The Fe-based coating was mainly composed of (Fe, Ni) and Cr7C3, and the Co-based coating was mainly composed of γ-Co and Cr23C6. It was found that the cooling rate of the CLC alloy coating was smaller than that of the USHLC, and the hardness of the CLC alloy coating was less than that of the USHLC. The average hardness of the UHSLC Fe-based and Co-based alloy coatings was 709 HV and 525 HV, respectively. The average hardness of the CLC Fe-based and Co-based alloy coatings was 615 HV and 493 HV, respectively. The rolling friction and wear tests were carried out with the CLC-treated and UHSLC-treated wheel specimens on the GPM-30 rolling contact fatigue testing machine. The results showed that the wear rate of the UHSLC alloy coating on the wheel specimens was significantly lower than that of the CLC alloy coating on the wheel specimens. The wear rates of the UHSLC Fe-based and Co-based alloy coatings on the wheel specimens were reduced by 40.7% and 73.8%, respectively. It was demonstrated that the wear resistance of the USHLC alloy coatings was better than those of the CLC alloy coatings. The CLC alloy coating exhibited more severe fatigue damage with small cracks. Furthermore, the damage of the UHSLC alloy coating was relatively minor, with slight spalling. The Co-based alloy coating exhibited superior wear properties with the same laser cladding process.

针对电力机车无火回送培训周期长、效率低、需占用实体机车的问题,提出一种基于VR技术的HXD1型电力机车无火回送实训系统.该系统借用虚拟现实等新兴技术,结合无火回送教学内容,构建逼真实训场景,交互式指导无火回送处理流程,快速培养员工的无火回送处置能力.该系统相较于传统机车无火回送实训具有明显优势,具备推广应用前景.

With the continuous expansion of road networks and the rise in railway transportation capacity, the scale of locomotive crew needs has increased sharply. However, the current training of locomotive crews is inefficient and costly and cannot meet the needs of the industry. This project adopted virtual reality technology to develop the driving and emergency skills of harmonious electric locomotive crews. The trainees can learn the composition and structure of the locomotive, master the working principles of the locomotive and receive training in relevant emergency skills in the virtual environment. After completion of the system development, we carried out a series of research studies and experiments; the results show that the use of this system to train personnel can lead to them quickly mastering the practical training content. This new training mode can effectively solve the outstanding problems with the current training system for locomotive crews.

以某地铁钢轨探伤车为研究对象,利用多体动力学软件UM建立车辆-轨道耦合动力学模型,分析探伤车在直线、曲线两种工况下的运行平稳性、安全性和曲线通过能力,为后续悬挂参数多目标优化提供原始数据支撑.基于MATLAB编程建立UM-ISight联合仿真平台,在ISight中以转向架一、二系悬挂刚度、阻尼等为目标参数,通过最优拉丁超立方法进行参数组合样本设计,并在此基础上构建符合精度要求的径向基函数神经网络(RBF-NN)近似模型,最后通过多目标优化算法NSGA-Ⅱ对近似模型进行寻优计算,得到最优的转向架悬挂参数组合.结果表明,所建近似模型具有较高的拟合精度,优化后车辆平稳性指标都得到明显改善,最多可降低35.39％,且车辆的脱轨系数、轮轨横向力、轮轴横向力、轮重减载率等曲线通过性能指标也有不同程度好转.

To analyze the transient micro-vibration phenomenon of wheel-rail contact surface in multi-degree of freedom during service of high-speed train, regarding wheel of S1002CN tread and rail of CN60 as the research objects, combined with finite element multi-flexible body dynamics (MFBD) technology and train-track coupling dynamics theory, a high-speed train-track spatial coupling vibration model under wheel-rail flexible-flexible (F-flex) contact mechanism was constructed. High speed wheel-rail contact vibration characteristics under the condition of variable friction coefficient and empty train are analyzed. The results show that the change of wheel-rail contact status under different influence factors will affect the lateral and vertical vibration acceleration and frequency of flexible wheel-rail in different degrees. The fluctuation of the wheel-rail vertical vibration acceleration of loaded train is greater than that of empty train, and the excitation frequency of loaded train is more than that of empty train. According to the vibration acceleration and vibration displacement of the integrated flexible rail, it can be seen that the vibration amplitude and fluctuation of the flexible rail are more obvious than that of the empty vehicle in both vertical and lateral direction.

曲线超高率与车辆运行状态有密切关系,为探究其对车辆-轨道梁耦合系统振动的影响规律,根据多体动力学理论且基于柔性曲线轨道梁,建立跨座式单轨车桥刚柔耦合系统动力学模型,研究不同曲线超高率和速度等级对轨道梁和车体动力响应的影响规律,并通过车辆水平轮径向力及车体侧滚角反映车辆运行状态变化.结果表明:当增大曲线超高率时,轨道梁跨中竖向位移响应值先减小后增大,车辆以40 km/h速度运行时,横向位移响应值逐渐朝向弯道内侧增大;当速度为50 km/h～65 km/h时,横向位移响应值由弯道外侧向内侧先减小后增加,车体横向动力响应所受影响比竖向动力响应更为显著.在合适的曲线超高率下运行有利于提高车辆运行品质.

为研究不同钢轨打磨处理和轮轨型面匹配对轨道结构振动特性影响,以国内某型号动车组和CRTSⅢ无砟轨道板为对象,基于车辆-无砟轨道-路基耦合系统动力学,借助于Ansys和UM软件建立动力学模型,分析不同速度下轮轨型面匹配对轨道板振动特性的影响.结果表明:轮轨型面磨耗会对接触特性产生显著影响,从单点接触变为多点接触,等效锥度和接触角增大,同时对轨道板结构的振动特性产生不同程度的影响.通过对钢轨进行打磨,可有效改善轮轨接触特性,轨道板的振动位移与加速度峰值下降,为打磨目标型面的后续改进提供指导.

实现了一个基于圆容栅传感器检测的电梯闸瓦嵌入式监测仪及其后台安全系统与报警系统.首先利用圆容栅传感器与单片机实现对电梯闸瓦磨耗量进行测量并输出.再通过W5100网络模块将电梯实时抱闸厚度发送到PC机中以进行电梯抱闸磨损情况的图样化处理,以达到对电梯抱闸磨耗进行实时监控的目的.