Due to the combined effects of material properties, manufacturing tolerances, and complex operational conditions, the wheel-rail interface and suspension parameters of high-speed trains exhibit significant stochasticity, which fundamentally shapes dynamic performance distributions. To capture this stochasticity over a full reprofiling cycle, this study integrates multi-condition wear simulations with measured data to establish a stochastic wheel-profile database. A method combining Monte Carlo simulation, Latin hypercube sampling, and Kullback-Leibler divergence is proposed to predict dynamic performance distributions while balancing sampling efficiency and convergence control. Analysis of the coupled effects of stochastic wheel-rail and suspension parameters shows that dynamic performance distributions evolve significantly with mileage. In the later service stage, key indicators such as the lateral Sperling index exhibit left-skewed and heavy-tailed distributions, with broader spreads and increased tail risk as mileage accumulates. The proposed method enables quantitative evaluation of long-term dynamic performance evolution and operational safety risk.

Timely and accurate fault diagnosis of suspension systems is paramount for ensuring the operational safety of rail vehicles. Recent years have witnessed extensive research in this field, primarily categorized into model-based and data-driven methods based on their underlying knowledge sources. Model-based methods rely on precise mathematical models to achieve fault detection and isolation via state or parameter estimation. Data-driven methods leverage historical and real-time data to extract fault features using statistical analysis, traditional machine learning, or deep learning techniques. This article presents a systematic review of these methods, offering a detailed comparison of their advantages and disadvantages, while summarizing current development trends and existing challenges. Finally, several future research directions are proposed, including model-data fusion diagnostic strategies, enhancement of real-time performance and robustness, enhancement of reliability prediction and uncertainty quantification, translation into engineering applications, and intelligent diagnostic techniques under small-sample conditions, aiming to provide references for the further development of suspension system fault diagnosis technology.

This study investigates frequency-selective stiffness (FSS) yaw dampers versus conventional dampers featuring the same bidirectional-flow structure for railway vehicle stability analysis. Unlike traditional designs, FSS dampers feature enhanced frequency-dependent dynamic characteristics, with dynamic stiffness varying substantially across different excitation frequencies, whereas conventional dampers exhibit only minor frequency-dependent variations. Bench tests were conducted on both damper types with identical static force-velocity characteristics. Results demonstrate that the FSS damper achieves 43.8% lower dynamic stiffness and 23.5% lower dynamic damping at carbody hunting frequencies, while maintaining comparable high dynamic stiffness at bogie hunting frequencies. A nonlinear damper model incorporating frequency- and amplitude-dependent behaviour was developed and validated against experimental data. Vehicle dynamics simulations reveal that the FSS damper provides superior carbody stability for 300 km/h-class vehicles under low-conicity scenarios. However, at 350 km/h, the carbody stability index approaches the threshold due to reduced dynamic damping - a limitation stemming from the damper's design principle. Parametric analysis suggests that maintaining adequate dynamic damping or reducing dynamic stiffness can improve carbody stability at ultra-high-speeds. Under high-conicity conditions, both damper types demonstrate comparable bogie stability. These findings demonstrate that FSS dampers offer effective solutions for balancing carbody and bogie stability, though careful parameter optimisation is essential for ultra-high-speed applications.

After two decades of leapfrog development, China has built the world's largest 350 km/h high-speed railway operation system. CR400 high-speed train, as the core equipment of this system, its wheel-rail dynamic characteristics directly affect the operational quality of the railway network. Based on a four-month in-service tracking test covering over 230,000 km on the Beijing-Shanghai high-speed line, this study identifies fixed-frequency peaks in vertical axlebox acceleration around 45 Hz, 350 Hz, 580 Hz, and 820 Hz, amplitudes of which surge nonlinearly with speed. Wheel roughness measurements reveal a distinctive "dual-peak order" polygonization pattern: dominated by the 10 similar to 12th order (similar to 350 Hz, wavelength 240 similar to 290 mm) with a co-developing 17 similar to 18th order (similar to 580 Hz, 160 similar to 170 mm), markedly different from the existing CRH380 trains. Through finite element simulations and wear evolution analyses, we establish that these frequencies correspond to intrinsic wheel-rail coupling modes (P2, B2, B3, B4), with the low-damping and frequency-stable B2 and B3 modes acting as the physical origins driving periodic wheel wear. To explain why low-order polygons become dominant despite the presence of both B2 and B3 excitations, we propose PolyFilter, a novel spatial convolution-based model that, for the first time, abstracts the abrasive block as a mechanical low-pass filter. The analysis reveals that the current 125-mm-long abrasive block operates in a severely ineffective trace-grinding mode against the 10-12th-order polygon at 350 km/h, nullifying their intended suppression effect while allowing the B3-excited high-order components to be partially suppressed. This geometric selectivity, rooted in the fundamental mismatch between block length and polygon wavelength, provides the first quantitative explanation for the selective evolution of polygon orders observed in field tests. Accordingly, two targeted countermeasures are proposed: (1) frequency-shifted arrangement of high-frequency vibration-absorbing fasteners to detune resonant excitation; and (2) increasing abrasive block length to achieve effective "span-grinding". These countermeasures offer both theoretical insight and practical pathways for ensuring safe, reliable, and cost-efficient operation of high-speed trains exceeding 350 km/h, including the CR400 and the under-development CR450.

[Objective] The actual operating conditions of high-speed EMUs (electric multiple units) are complex, and the vibration acceleration signals of axlebox bearings often exhibit strong nonlinear and non-stationary features, with significant background noise interference. Consequently, traditional fault diagnosis methods struggle to accurately extract the fault characteristic frequencies. Therefore, it is necessary to propose a fault detection method for high-speed train axlebox bearings based on envelope synthesis. [Method] The detection concept of the fault detection method for high-speed EMU axlebox bearings based on envelope synthesis is elaborated as follows: First, the axlebox vibration acceleration signal is decomposed into several IMF (intrinsic mode functions) using VMD (variational mode decomposition) algorithm. Then, Hilbert transform is applied to each IMF to obtain its envelope. Next, all IMF envelopes are synthesized and reconstructed, and the reconstructed signal undergoes Fourier transform to obtain the spectrum of the vibration signal under strong background noise. Finally, a fault detection is carried out by determining whether the dominant frequency in the spectrum coincides with the fault characteristic frequency of the high-speed EMU axlebox bearings. The effectiveness and robustness of the proposed method are verified using simulated signals and actual fault bearing data from real vehicles. [Result & Conclusion] The fault detection method for high-speed EMU axlebox bearings based on envelope synthesis can effectively reduce the influence of background noise and significantly enhance the vibration amplitude of fault characteristic frequencies in the spectrum.

High-speed trains in China are characterized by high operating speeds, extensive constant-speed sections, prolonged durations of continuous operation, and reliance on ballastless track systems with limited structural variations, resulting in frequent occurrences of high-order wheel polygonization. To fundamentally solve this issue, researchers have conducted in-depth studies on the formation mechanism behind this special phenomenon. However, the academic community has not yet reached a consensus after a decade of research since the systematic study was initiated in 2014. Current studies propose four main mechanisms for high-order wheel polygonalization of high-speed trains, all originating from Southwest Jiaotong University (SWJTU): (A) vibration of bogie components; (B) friction-induced self-excited vibration; (C) resonance of the wheel-rail system triggered by the third-order bending modal vibration of a rail segment restrained by the bogie (rail B3 modal vibration); and (D) joint action of rail B3 modal vibration and frequency shift. This paper first sorts out the controversial points existing in these four mechanisms, and then systematically proves the following two points through vehicle tracking tests, rail modal tests, and dynamics simulation analysis: (1) The rail B3 mode is the fundamental factor leading to the high-order wheel polygonization, and there is no significant frequency shift between the B3 modal frequency and the eventually formed polygonal wheel passing frequency (PWP frequency). (2) The parameter-matching characteristics of the fastener system significantly regulate the B3 modal vibration, with optimized parameter-matching relationships proving effective in suppressing wheel polygonization. This research not only clarifies the formation mechanism of high-order wheel polygonization in high-speed trains but also provides an innovative theoretical foundation and engineering solution for addressing this persistent issue.

Yaw dampers are usually tuned according to the nominal static force-velocity (F-V) characteristics specified by the vehicle manufacturer. However, in the low-speed region, the static F-V curve often varies significantly among damper suppliers due to a lack of standardised specifications. This variability underscores the necessity of accounting for nonlinearities in the analysis and understanding their relationship with the dynamic behaviour of yaw dampers and their impact on overall vehicle stability. To address these issues, we developed and validated a simplified physical damper model that incorporates these undefined nonlinearities through rig testing. Using this model, simulations were performed to investigate how these nonlinearities affect dynamic damper characteristics and vehicle stability. Our findings indicate that undefined nonlinearities in the low-speed region of the static F-V curve significantly influence the dynamic stiffness and damping of yaw dampers, particularly at small amplitudes and low frequencies. These effects have a pronounced impact on carbody stability in conditions with low wheel-rail conicity and significantly affect passenger's comfort. The insights gained from this study offer valuable implications for the design, optimisation and standardisation of yaw dampers in high-speed trains.

The traction drive system of a high-speed train is the key subsystem of a high-speed train, which controls the driving and braking of the train through electromechanical coupling, and determines the running speed, power quality, and comfort of the train. The traction drive system is subjected to many internal and external excitation sources and frequency bandwidth, and the dynamic response and dynamic characteristics of the system are extremely complex. The results of the traction motor field vibration test showed that 100 Hz vibration frequency occurred during traction and braking of high-speed trains when the inverter output voltage frequency was close to 100 Hz, and its vibration amplitude was higher than other frequency bands. When traction power was cut-off, the 100 Hz frequency was not significant. Through simulation analysis of fatigue damage, it was found that 100 Hz DC-link voltage pulsation would aggravate the fatigue damage of the motor hanger. The line vibration test and bench test of the gearbox showed that there was a natural frequency of the gearbox at about 2500 Hz, when the meshing frequency was close to it. Thus, the resonance characteristic became significant.

This paper proposes a fast and stable iterative algorithm for wheel-rail contact geometry based on constraint equations, which can be implemented in dynamic wear simulations that real-time profile updating is needed. Further, critical factors that determine convergence and iteration stability are analyzed. A B-spline is adopted for wheel-rail profile modeling because it does not contribute to changes in the global shape of curves. It is found that the smoothness of the first and second derivative curves significantly affects the numerical stability of the Jacobian matrix, which determines the increments in iterations. Moreover, a damped Newton's iteration formula with a scaling factor of 0.5 is proposed considering the convergence rate and out-of-bound issues for the updated step. The influence of the initial iteration parameters on the convergence is studied using Newton fractals. The range within +/- 3mm, centered on the target contact point, is found to be an unconditionally stable domain. The proposed method could achieve convergence within 10 and 30 steps under thread and flange contact conditions, respectively.

CRH5型动车组万向轴不平衡故障不仅会引起动车组异常振动和噪声,还会降低轴承的使用寿命.对于部分未安装车载万向轴传动系统监控装置的CRH5型动车组,基于万向轴振动传递路径,分析了大量实测样本数据,提出利用车体地板垂向振动状态间接判断万向轴不平衡故障的方法,并提出了相应的判断标准.该检测方法以车体地板垂向40～65 Hz时域振动加速度滤波信号均方根值和40～65 Hz滤波信号在原始信号中的占比作为判断指标.相关测试结果显示,该检测方法能有效判断万向轴不平衡故障.

通过研究大量的高速列车轴承温度实测历史数据,提出了利用轴承温度关联测点之间的变化相似作为实现轴承温度状态连续性识别的依据.首先,应用无监督学习One-class SVM算法进行异常识别,结果表明该算法虽然可以较好地识别异常即"紧急"状态,但无法实现轴承温度状态的连续性识别.然后,进一步地根据相对温度变化将轴承从正常到故障失效整个过程定义为4个阶段,即长、中、短、紧急,并提出了以包含相对温度及温变速率等信息的图像样本来描述轴承温度状态变化的数据表达方式.最后,应用深度学习CNN算法进行轴承温度状态的连续性识别,结果表明CNN模型能够可靠地识别轴承温度的不同状态,从而为实现高速列车轴承预测性维护提供了一定支撑.

针对高速动车组车轮多边形问题的研究表明,硬度偏低的车轮更易出现车轮多边形,因此考虑从提高车轮硬度方向来缓解抑制车轮多边形的产生和发展.经过研究,制定了在不落轮旋床上进行滚压车轮踏面以提升车轮踏面外表面硬度的措施,并跟踪滚压后车轮的运用状态,经过2个旋修周期的跟踪,结果显示,滚压会使车轮表面显微组织得到细化,使踏面初始产生压应力,抑制车轮多边形的产生和发展.

高速列车长期服役跟踪测试发现,电机弹性架悬高速转向架蛇行频率随运行里程增加存在跳变现象.为了探明其原因,建立电机弹性架悬转向架横向动力学模型,研究等效锥度变化对蛇行频率的影响.通过系统特征值根轨迹分析对该现象进行机理性研究,并利用时域仿真进行数值验证.分析电机架悬参数对蛇行频率跳变特征的影响,揭示蛇行频率跳变的存在条件.研究结果表明,电机横移频率过小或过大,转向架蛇行频率跳变现象消失;电机横移阻尼比过大,转向架蛇行频率跳变现象也会消失.当电机架悬参数满足蛇行频率跳变条件时,电机横移频率和阻尼比对蛇行频率跳变时的临界等效锥度、频率跳变幅值等有较大影响.

建立某高速列车单循环作用式抗蛇行减振器的简化物理参数模型,该模型基于Kasteel复杂物理参数模型,但对阻尼阀和单向阀进行了合理简化.针对抗蛇行减振器的静、动态特性,进行仿真和台架试验的对比研究.最后,对比研究了简化物理参数模型和传统Maxwell模型下的车辆蛇行运动稳定性.研究结果表明,传统Maxwell模型无法准确描述液压减振器的动态特性,而复杂物理参数模型虽计算精度高但其效率过低无法用于整车动力学仿真.简化物理参数模型能够准确地模拟抗蛇行减振器的复杂力学行为,计算效率高,适于整车动力学仿真.在高锥度工况下,传统Maxwell模型会过高估计单循环作用式抗蛇行减振器的动态阻尼,导致稳定性预测结果和简化物理参数模型有较大误差.

Motor hangers are key components of high-speed trains. Local cracking of motor hangers in service is investigated in this study by employing fracture analysis, on-track tests, finite element analysis, etc., to determine the cause of failure. Macro- and microfracture analyses show that cracks originate from weld defects and propagate to the external surface of the lower cover plate. Time domain, frequency domain, and time-frequency domain analyses indicate that both of the dominant frequencies of the stress and the vibration acceleration are 100 Hz, which are consistent with the pulsating torque frequency. The pulsating torque of the motor induced by the current fluctuation excites the resonance mode of the motor hanger, which brings out the possibility of fatigue crack initiation and propagation under operating conditions. The results show that welding defects reduce the fatigue strength of the weld, and high-amplitude and high-frequency vibrations caused by the pulsating torque of the motor eventually lead to the failure of the motor hanger. The study provides a reference for the structural design of a bogie drive system.

为了研究高速动车组转向架区域的积雪结冰问题,针对简化的车体和转向架模型,采用三维非定常雷诺时均Realizable k-ε 湍流模型(URANS),耦合离散相模型(DPM)流场仿真计算,模拟高速动车组转向架区域流场和雪粒子分布情况.研究结果表明:转向架底部高速气流携带雪花从转向架中部和后方向上折返进入转向架上方区域,并形成低速漩涡,雪花在狭窄处逐渐堆积;转向架底部各零部件迎风侧表面受到气流直接冲击,表面呈现较为明显的正压,在发热零件表面极易形成积雪积冰.另外,沿着列车运行方向,后3台拖车转向架比第1台拖车转向架表面的粒子黏附情况依次减少56.43％,95.42％,95.47％,第2台动车转向架比第1台动车转向架表面黏附粒子数减少51.74％.

复兴号CR400BF高速动车组动力转向架的牵引电机采用特有的四点弹性架悬方式,在电机和构架之间安装有横向液压减振器和横向止挡,首次采用牵引电机作为动力吸振器来控制转向架蛇行运动稳定性和蛇行频率,从而避免引起车体弹性模态共振;考虑悬挂参数和轮轨接触非线性,建立了复兴号动车组非线性多刚体动力学仿真模型,通过悬挂模态计算和动力学时域仿真,分析了关键参数对动车蛇行运动的影响规律;基于将电机作为动力吸振器的原理,优化了电机节点横向刚度和横向减振器阻尼;考虑动车组运营中的轮轨匹配随机因素,组合400种轮轨随机匹配状态,仿真分析了动车的动力学性能;开展动车组长期线路动力学跟踪试验,研究了动力转向架蛇行运动演变规律。仿真与试验结果表明:牵引电机弹性架悬下的构架横向加速度频谱图从以蛇行频率为主频的单峰值变化为主频在蛇行频率两侧的双峰值,说明电机起到了动力吸振器的作用;将电机作为动力吸振器能够提高动车蛇行运动稳定性,具有不同等效锥度的典型轮轨匹配下非线性临界速度超过500 km·h -1 ;动车蛇行运动最高频率被控制在6 Hz附近,远离车体中部菱形弹性模态频率8.5 Hz,避免了转向架蛇行运动激起车体弹性共振;动车组在轨道随机不平顺激扰下,构架端部横向加速度小于0.5g,平稳性指标小于2.5,轮轴横向力和脱轨系数等运行安全性指标满足要求。

利用SIMPACK软件建立了某出口铁路客车动力学计算模型,通过现场扭曲试验验证了计算模型的正确性.

针对高速动车组出现的车辆异常振动问题,聚焦故障出现的位置,分析引起异常振动的因素.通过振动传递路径分析、车辆振动测试、振动数据处理及故障零部件分解检查,找到异常振动根本原因.因动车组牵引传动系统零部件出现缺陷,导致转向架在正常运行过程中出现异常振动,并通过悬挂系统传递到车体,引起车体异常抖动.