Torque effect on vibration behavior of high-speed train gearbox under internal and external excitations

The high-speed train transmission system,experiencing both the internal excitation originating from gear meshing and the external excitation originating from the wheel-rail interaction,exhibits complex dynamic behavior in the actual service environment.This paper focuses on the gearbox in the high-speed train to carry out the bench test,in which various operat-ing conditions(torques and rotation speeds)were set up and the excitation condition covering both internal and external was created.Acceleration responses on multiple positions of the gearbox were acquired in the test and the vibration behavior of the gearbox was studied.Meanwhile,a stochastic excitation modal test was also carried out on the test bench under different torques,and the modal parameter of the gearbox was identified.Finally,the sweep frequency response of the gearbox under gear meshing excitation was analyzed through dynamic modeling.The results showed that the torque has an attenuating effect on the amplitude of gear meshing frequency on the gearbox,and the effect of external excitation on the gearbox vibration cannot be ignored,especially under the rated operating condition.It was also found that the torque affects the modal param-eter of the gearbox significantly.The torque has a great effect on both the gear meshing stiffness and the bearing stiffness in the transmission system,which is the inherent reason for the changed modal characteristics observed in the modal test and affects the vibration behavior of the gearbox consequently.

Advancing high-speed train gearbox durability:enhanced bearing load and contact stress through transition from helical to herringbone gears

High-speed trains typically utilize helical gear transmissions,which significantly impact the bearing load capacity and fatigue service performance of the gearbox bearings.This paper focuses on the gearbox bearings,establishing dynamic models for both helical gear and herringbone gear transmissions in high-speed trains.The modeling particularly emphasizes the precision of the bearings at the gearbox's pinion and gear wheels.Using this model,a comparative analysis is conducted on the bearing loads and contact stresses of the gearbox bearings under uniform-speed operation between the two gear transmissions.The findings reveal that the helical gear transmission generates axial forces leading to severe load imbalance on the bearings at both sides of the large gear,and this imbalance intensifies with the increase in train speed.Consequently,this results in a significant increase in contact stress on the bearings on one side.The adoption of herringbone gear transmission effectively suppresses axial forces,resolving the load imbalance issue and substantially reducing the contact stress on the originally biased side of the bearings.The study demonstrates that employing herringbone gear transmission can significantly enhance the service performance of high-speed train gearbox bearings,thereby extending their service life.

Dynamics and Fault Diagnosis of Railway Vehicle Gearboxes:A Review

The railway vehicle gearbox is an important part of the railway vehicle traction transmission system which ensures the smooth running of railway vehicles.However,as the running speed of railway vehicles continues to increase,the railway vehicle gearbox is exposed to a more demanding operating environment.Under both internal and external excitations,the gearbox is prone to faults such as fatigue cracks,and broken teeth.It is crucial to detect these faults before they result in severe failures and accidents.Therefore,understanding the dynamics and fault diagnosis of railway vehicle gearbox is needed.At present,there is a lack of systematic review of railway vehicle gearbox dynamics and fault diagnosis.So,this paper systematically summarizes the research progress on railway vehicle gearbox dynamics and fault diagnosis.To this end,this paper first summarizes the latest research progress on the dynamics of railway vehicle gearboxes.The dynamics and vibration characteristics of the gearbox are summarized under internal and external excitations,as well as faulty conditions.Then,the stateof-the-art signal processing and artificial intelligence methods for fault diagnosis of railway vehicle gearboxes are reviewed.In the end,future research prospects are given.

Measured load spectra of the bearing in high-speed train gearbox under different gear meshing conditions

The load spectrum is a crucial factor for assess-ing the fatigue reliability of in-service rolling element bear-ings in transmission systems.For a bearing in a high-speed train gearbox,a measurement technique based on strain detection of bearing outer ring was used to instrument the bearing and determine the time histories of the distributed load in the bearing under different gear meshing conditions.Accordingly,the load spectrum of the total radial load car-ried by the bearing was compiled.The mean value and class interval of the obtained load spectrum were found to vary non-monotonously with the speed and torque of gear mesh-ing,which was considered to be caused by the vibration of the shaft and the bearing cage.As the realistic service load input of bearing life assessment,the measured load spectrum under different gear meshing conditions can be used to pre-dict gearbox bearing life realistically based on the damage-equivalent principle and actual operating conditions.

An Improved Coupled Dynamic Modelling for Exploring Gearbox Vibrations Considering Local Defects

Gearbox is a key part in machinery,in which gear,shaft and bearing operate together to transmit motion and power.The wide usage and high failure rate of gearbox make it attract much attention on its health monitoring and fault diagnosis.Dynamic modelling can study the mechanism under different faults and provide theoretical foundation for fault detection.However,current commonly used gear dynamic model usually neglects the influence of bearing and shaft,resulting in incomplete understanding of gearbox fault diagnosis especially under the effect of local defects on gear and shaft.To address this problem,an improved gear-shaft-bearing-housing dynamic model is proposed to reveal the vibration mechanism and responses considering shaft whirling and gear local defects.Firstly,an eighteen degree-of-freedom gearbox dynamic model is proposed,taking into account the interaction among gear,bearing and shaft.Secondly,the dynamic model is iteratively solved.Then,vibration responses are expounded and analysed considering gear spalling and shaft crack.Numerical results show that the gear mesh frequency and its harmonics have higher amplitude through the spectrum.Vibration RMS and the shaft rotating frequency increase with the spalling size and shaft crack angle in general.An experiment is designed to verify the rationality of the proposed gearbox model.Lastly,comprehensive analysis under different spalling size and shaft crack angle are analysed.Results show that when spalling size and crack angle are larger,RMS and the amplitude of shaft rotating frequency will not increase linearly.The dynamic model can accurately simulate the vibration of gear transmission system,which is helpful for gearbox fault diagnosis.

Wind Turbine Planetary Gearbox Fault Diagnosis via Proportion-Extracting Synchrosqueezing Chirplet Transform

Wind turbine planetary gearboxes usually work under time-varying conditions,leading to nonstationary vibration signals.These signals often consist of multiple time-varying components with close instantaneous frequencies.Therefore,high-quality time-frequency analysis(TFA)is needed to extract the time-frequency feature from such nonstationary signals for fault diagnosis.However,it is difficult to obtain high-quality timefrequency representations(TFRs)through conventional TFA methods due to low resolution and time-frequency blurs.To address this issue,we propose a new TFA method termed the proportion-extracting synchrosqueezing chirplet transform(PESCT).Firstly,the proportion-extracting chirplet transform is employed to generate highresolution underlying TFRs.Then,the energy concentration of the underlying TFRs is enhanced via the synchrosqueezing transform.Finally,wind turbine planetary gearbox fault can be diagnosed by analysis of the dominant time-varying components revealed by the concentrated TFRs with high resolution.The proposed PESCT is suitable for achieving high-quality TFRs for complicated nonstationary signals.Numerical and experimental analyses validate the effectiveness of the PESCT in characterizing the nonstationary signals from wind turbine planetary gearboxes.

Numerical Visualization of Grease Flow in a Gearbox

Lubricating greases are widely used in e.g.open gear drives and gearboxes with difficult sealing conditions.The efficiency and heat balance of grease-lubricated gearboxes depend strongly on the lubrication mechanisms channeling and circulating,for which the grease flow is causal.The computational fluid dynamics opens up the possibility to visualize and understand the grease flow in gearboxes in more detail.In this study,a single-stage gearbox lubricated with an NLGI 1-2 grease was modeled by the finite-volume method to numerically investigate the fluid flow.Results show that the rotating gears influence the grease sump only locally around the gears.For a low grease fill volume,the rotation of the gears is widely separated from the grease sump.For a high grease fill volume,a pronounced geargrease interaction results in a circulating grease flow around the gears.The simulated grease distributions show good accordance with high-speed camera recordings.

Intelligent Fault Diagnosis for Planetary Gearbox Using Transferable Deep Q Network Under Variable Conditions with Small Training Data

Effective fault diagnosis of planetary gearboxes is critical for ensuring the safety and dependability of mechanical drive systems.Nevertheless,variable conditions and inadequate fault data bring huge challenges to its practical fault diagnosis.Taking this into account,this study presents a new intelligent fault diagnosis(IFD)approach for planetary gearbox using a transferable deep Q network(TDQN)that merges deep reinforcement learning(DRL)and transfer learning(TL).First,a DRL environment simulation is designed by a predefined classification Markov decision process.Then,leveraging varied-size convolutions and residual learning,a multiscale residual convolutional neural network agent for TDQN is created to automatically learn meaningful features directly from vibration signals while avoiding model degradation.Next,a large source dataset is obtained from complex conditions,and this agent learns an IFD policy via autonomous interaction with the data environment.Finally,a parameter-based TL strategy is adopted to retrain the model on target datasets with variable conditions and small training data,which is conducted by fine-tuning the model parameters gained from the source task to accomplish target tasks.The results show that this TDQN outperforms not only state-of-the-art methods in a source task with an accuracy of 98.53%but also in two target tasks with 99.63%and 98.37%,respectively.

Multi-objective Optimization of a Two-Stage Helical Gearbox to Improve Efficiency and Reduce Height

The goal of this research is to look at multi-target optimization of a two-stage helical gearbox in order to determine the best key design elements for reducing gearbox height and enhancing gearbox efficiency.To do this,the method known as Taguchi and GRA(Grey Relation Analysis)were used in two stages to address the problem.The single-objective optimization problem was addressed first to close the gap between variable levels,and then the multi-objective optimization problem was solved to determine the best primary design variables.The first and second stage CWFWs(Coefficients of Wheel Face Width),ACS(Permissible Contact Stresses),and first stage gear ratio were also calculated.The study’s findings were utilized to identify the best values for five critical design aspects of a two-stage helical gearbox.

Characterization,identification and life prediction of acoustic emission signals of tensile damage for HSR gearbox housing material

Purpose-This study aims to ensure the operation safety of high speed trains,it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time,yet the traditional tests of mechanical property can hardly meet this requirement.Design/methodology/approach-In this study the acoustic emission(AE)technology is applied in the tensile tests of the gearbox housing material of an high-speed rail(HSR)train,during which the acoustic signatures are acquired for parameter analysis.Afterward,the support vector machine(SVM)classifier is introduced to identify and classify the characteristic parameters extracted,on which basis the SVM is improved and the weighted support vector machine(WSVM)method is applied to effectively reduce the misidentification of the SVM classifier.Through the study of the law of relations between the characteristic values and the tensile life,a degradation model of the gearbox housing material amid tensile is built.Findings-The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process,and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%.The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.Originality/value-The results of this study provide new concepts for the life prediction of tensile samples,and more further tests should be conducted to verify the conclusion of this research.

面向复杂工况的液力变矩器与变速箱效能优化协同控制策略

针对工程机械在复杂工况下能源利用率不高的问题,提出了一种液力变矩器与变速箱效能优化协同控制策略。首先,在装载机功率与力学平衡的基础上,构造了基于牵引性能理论的装载机动力传动系统仿真平台,并通过装载机循环工况采集到的实验数据验证了其有效性和可靠性;其次,基于发动机与液力变矩器台架实验,研究了ZL50型装载机的牵引特性,对比了其在起步、举升、铲掘、匀速4种不同牵引阶段的动力性能,分析了液力变矩器的闭锁点对装载机动力性、平顺性和经济性的影响;最后,提出了面向复杂工况的液力变矩器与变速箱效能优化协同控制策略,并对优化前后的装载机效能进行了对比分析。研究结果表明:在相同的V型循环工况下,采用所提出的控制策略,装载机等油耗输出能量从372.69 kW·h·mL^(-1)提高到420.51 kW·h·mL^(-1),整机效能提高了约12.8%。所提策略可为大型工程机械传动效率低、燃料消耗大、污染排放严重等问题提供解决方案。

基于遗传算法的风电齿轮传动系统参数优化设计

齿轮箱是风力发电系统的重要部件,其体积大、重量高等问题制约了风电清洁能源的发展.以1.5 WM风力发电机组为研究对象,建立齿轮传动系统轻量化数学模型,并采用遗传算法进行优化求解.结果表明,采用遗传算法是可行的,优化后齿轮传动系统体积减少了4.59%,这将进一步减小齿轮传动系统箱体体积和总质量.通过计算行星轮系的传动效率,验证了优化结果的可行性,该研究将为风电齿轮传动系统轻量化设计提供新方法.

改进MFO-LSTM网络的风电机组齿轮箱故障预警研究

风电机组齿轮箱在数据采集与监控系统(SCADA)的帮助下,通过监控齿轮箱油温是否超过阈值实现故障报警,其判断精度不高且问题发现不及时,因此使用长短期记忆网络模型(LSTM)融合SCADA数据实现对齿轮箱油温状态的预测。用齿轮箱正常运行状态下的数据训练LSTM模型,计算油温预测值与真实值之间的残差,根据正态分布的原则设置残差的上下预警阈值,用来对齿轮箱故障进行预警。为简化训练模型的复杂度,在SCADA数据中选用与齿轮箱油温相关性较为密切的参数作为LSTM模型的输入项。为降低因LSTM模型超参数设置不当造成的预测准确度表现不佳,提出改进飞蛾火焰算法(MFO)与LSTM的组合模型,在保留MFO算法强大的全局搜索能力的同时,使其避免陷入局部搜索的陷阱,通过改进MFO对LSTM模型参数进行迭代优化,最终构建合适的模型。最后通过某风电机组SCADA数据验证该方法能够有效预警齿轮箱的故障,并且与其他方法相比准确度更高,预警更及时,迭代效果更好。

基于多模 态集成卷积神经网络的数控机床齿轮箱故障诊断

针对数控机床齿轮箱在实际工作环境中负载多变且噪声干扰大、传统神经网络难以充分提取信号中的故障特征等问题,提出一种多模态集成卷积神经网络(MECNN)用于数控机床齿轮箱故障诊断。该方法将多模态融合技术与多个卷积神经网络结合,利用快速傅里叶变换方法将时域信号转换成频域信号;利用时域信号和频域信号对2个卷积神经网络进行训练,使模型能够分别从时域和频域2个角度提取特征,再将浅层特征融合;最后,将融合后的特征输入到卷积神经网络中进行故障特征的深度挖掘,并进行故障诊断。使用东南大学的齿轮箱数据集进行验证,设计了2种特征融合的方法并进行了对比。实验结果表明:在噪声下,MECNN模型用于故障诊断的准确性和鲁棒性均优于单一的时域CNN和频域CNN。

应用滑动轴承的风电齿轮箱行星轮系动力学建模及解耦方法

在行星轮系动力学建模中,常以非线性油膜力或线性刚度-阻尼形式考虑其对系统动力学特性的影响,前者仿真精度高但计算成本也高,后者计算效率高却忽略了油膜力和轴颈-轴套偏心量的时变性,仿真精度有限。为此,以2MW级风电齿轮箱为研究对象,建立滑动轴承时变线性刚度-阻尼模型,提出计入轴颈-轴套时变偏心量的滑动轴承附加偏心修正力计算方法;利用行星架销轴-行星轮变形协调关系,将时变线性刚度-阻尼模型与附加偏心修正力进行耦合;建立应用滑动轴承的风电齿轮箱行星轮系动力学模型,对比了工况和轴承参数对模型计算精度与系统动态响应的影响,并通过试验加以验证。研究结果表明,齿轮副动态啮合力波动会使滑动轴承刚度-阻尼系数和附加偏心修正力产生周期性变化;在稳定和瞬态工况下,提出的模型可以很好地预测系统响应,尤其是行星轮振动响应;减小滑动轴承宽径比与间隙、增大输入转矩可以改善系统均载性能。

风电机组用滑动轴承研究现状与发展趋势

首先,分析滚动轴承在风电传动系统中应用的局限性,探讨滑动轴承“以滑代滚”的可行性。然后,从风电机组用滑动轴承结构设计、轴承材料、性能分析及优化、试验测试及应用4个关键环节,综述滑动轴承风电应用的现有技术手段和面临的困难。最后,对风电机组滑动轴承的材料改性、一体化设计、延寿技术发展趋势做出展望。

高铁齿轮箱飞溅润滑性能数值模拟与型腔结构参数优化

以高铁齿轮箱齿轮啮合处平均压力和输出端集油盒处润滑油平均体积分数为评价指标,通过流体仿真软件ANSYS CFX,采用浸入实体法,建立了高铁齿轮箱斜齿轮飞溅润滑的CFD数值仿真模型。运用响应面Box-Behnken实验研究得出了径向距离、轴向距离、导油筋宽度对集油盒处润滑油平均体积分数和齿轮啮合处平均压力的二次回归模型,并对设计参数进行了优化。结果显示,最优参数为径向距离22.92 mm、轴向距离50 mm、导油筋宽度14.02 mm时,集油盒处润滑油平均体积分数为0.547,齿轮啮合处平均压力为32.452 kPa。

斜齿轮啮合弯矩对风电齿轮箱行星轮滑动轴承瞬态润滑性能影响分析

风电齿轮箱行星轮滑动轴承常被设计为以行星轮内孔和销轴分别作为轴套和轴颈,然而斜齿轮啮合弯矩容易使行星轮与销轴之间产生轴线不对中,导致边缘接触风险高,影响运行寿命。以6 MW级传动链齿轮箱行星轮滑动轴承为研究对象,考虑滑动轴承径向载荷、弯矩以及转速的动态影响,建立了行星轮滑动轴承瞬态摩擦-动力学耦合模型,并以风电机组传动链SIMPACK动力学模型提取的行星轮动态啮合力与时变转速作为行星轮滑动轴承的载荷与运动边界输入,分析了斜齿轮啮合弯矩、输入扭矩和滑动轴承半径间隙对行星轮滑动轴承润滑性能的影响规律,并进行了实验验证。研究结果表明,行星轮动态啮合力及其产生的啮合弯矩会造成行星轮轴心位置与偏斜角产生动态循环变化,并且随着载荷的增加,行星轮滑动轴承油膜/固体接触压力与不对中弯矩会逐渐增大;减小行星轮滑动轴承半径间隙可以有效提高其瞬态润滑性能。

变转速下基于改进多阶概率方法的风电齿轮箱故障诊断研究

阶次跟踪是一种有效的解决变转速故障诊断问题的方法,其关键前提是存在转速信号作为参考。然而,由于强背景噪声和弱谐波关系的影响,现有转速估计方法的准确性和自适应性有待进一步提高。因此,提出一种融合多传感器信号的改进多阶概率方法(MOPA)用以估计瞬时转速。首先,依据不同传感器信号的基频统一性和主导分量差异性,通过时频图瞬时切片归一化融合的方式,构建具有强谐波关系的时频图;其次,为消除时变工况下时频图中横纵方向上的间歇恒频和短时宽频背景噪声,提出滑动消噪方法;最后,基于处理后的时频图执行MOPA,实现瞬时转速自动估计,结合阶次跟踪解决风电齿轮箱变转速故障诊断问题。经实测数据验证,改进MOPA估计的瞬时频率的准确性和自适应性均优于对方法,平均绝对百分比误差为0.56%,均小于对比方法的15.73%、13.99%和1.21%。结合阶次分析诊断了变转速下风电齿轮箱异常。

新型轿车电动玻璃升降器箱与盖注塑模设计

新型轿车电动玻璃升降器的塑料箱与盖,是升降器传动机构的组装与支撑件。箱与盖的孔与孔位精度,决定着传动机构的性能和精度。在提供了样品的情况下,接收方提出供给方所生产的升降器要与进口产品能通用的要求。箱与盖注塑模设计的过程中,根据样品上模具结构成型痕迹和注塑件的形体分析,确定了注塑模的结构。实践证明,所加工的箱和盖与样品达到了一致,实现了与进口件混装的要求。