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.

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.

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.

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.

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。

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。

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。

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.

基于IPSO-MCKD的汽车变速箱轴承故障诊断

针对车辆在城区运行过程中频繁启停造成变速箱滚动轴承故障易发的问题,在轴承故障诊断中引入最大相关峭度反卷积(Maximum Correlation Kurtosis Deconvolution,MCKD)的方法,为了避免过于依赖人工选择MCKD算法中滤波器系数和移位数,提出了一种参数自适应的最大相关峭度反卷积的故障诊断方法。该方法以输入信号的包络谱中最大相关峭度为目标函数,采用改进后的粒子群优化(Improved Particle Swarm Optimization,IPSO)算法优化MCKD中的滤波器系数和位移数,最后通过对故障信号的包络谱进行分析,提取轴承的故障特征。仿真和试验的结果表明,该方法可以有效降低环境中的噪声干扰,准确从强噪声中提取故障特征,实现故障诊断。

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。

考虑齿面闪温的风电齿轮箱裂纹故障特征分析

为了研究齿面闪温对风电齿轮箱裂纹故障时系统动力学响应的影响,利用Blok闪温理论分析齿轮啮合时的齿面温度,应用热变形公式计算齿廓形变,进而通过Hertz接触理论获得计及齿面闪温影响的轮齿刚度。考虑齿面闪温、阻尼、时变啮合刚度、综合啮合误差和齿侧间隙,建立含有高速级齿轮齿根裂纹的齿轮箱扭转动力学模型。通过时域图、频谱图、相图和Poincaré截面分析不同裂纹长度下系统振动特性随齿面闪温变化的规律,并比较裂纹故障仿真与实验的时频域结果。结果表明:齿面闪温使时域图上裂纹产生的冲击幅值增大、频域图中故障边频结构更为复杂、相图曲线向外扩展以及Poincaré截面离散点增多,且变化均随裂纹长度的增加越为明显。研究结论可为齿轮裂纹故障状态的诊断与监测提供依据。

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。

基于RCMFME和AO-ELM的齿轮箱损伤识别策略

针对模糊熵只考虑信号的局部特征而忽略信号的全局特征,导致齿轮箱故障识别的准确率不佳的问题,提出了一种基于精细复合多尺度模糊测度熵(RCMFME)、天鹰优化器(AO)优化极限学习机(ELM)的齿轮箱故障诊断方法。首先,在精细复合多尺度模糊熵的基础上,对矢量的构造方式进行了改进,提出了能够同时考虑时间序列局部特征和全局特征的RCMFME方法;随后,利用RCMFME指标提取了齿轮箱振动信号的熵值,组建了故障特征向量;接着,利用AO算法对极限学习机的参数进行了自适应搜索,生成了参数最优的多类别分类器;最后,将训练样本的故障特征向量输入至AO-ELM分类模型中进行了模型训练,以构造性能最优的分类器,并实现了对齿轮箱测试样本的故障识别目的;利用两种齿轮箱振动数据集进行了实验,在识别准确率和识别稳定性方面,与相关的特征提取方法进行了对比。研究结果表明:采用基于RCMFME和AO-ELM的故障诊断方法能够分别取得100%和98%的分类准确率,平均识别准确率分别达到了100%和98%,优于精细复合多尺度全局模糊熵(RCMGFE)、精细复合多尺度模糊熵(RCMFE)、精细复合多尺度样本熵(RCMSE)。该方法具有显著的应用潜力。

基于模糊PID汽车变速箱自动控制方法

为改善汽车变速箱的目标速度跟踪水平,提升汽车的动力性,基于模糊PID汽车变速箱自动控制方法,确定汽车行驶的目标车速,计算目标车速与实际车速之间的偏差,以及速度偏差的变化率,将上述结果作为PID控制器的输入,利用参数整定后的PID控制器,确定汽车变速箱的控制变量,控制汽车传动机构输出理想车速。实验结果表明,汽车以阶跃信号、斜坡信号和正弦信号状态行驶时,该方法均可以精准跟踪汽车变速箱的速比,控制效果与动态跟踪性能优异,为保持汽车的最佳动力性能提供了良好的控制策略。

EHDE和WHO-SVM模型在齿轮箱故障诊断中的应用

针对现有齿轮箱故障诊断方法对数据长度敏感的缺陷,提出了一种基于增强层次多样性熵(EHDE)和野马算法(WHO)优化支持向量机(SVM)的齿轮箱故障诊断模型。首先,传统熵值特征提取方法在特征提取阶段对数据样本的长度比较敏感,为此提出了增强层次多样性熵,并将其作为特征提取指标用于提取齿轮箱的故障特征;其次,采用WHO算法对SVM模型的参数进行了优化,建立了参数最优的WHO-SVM分类器;最后,将故障特征样本输入至WHO-SVM分类器中进行了训练和识别,完成了样本的故障识别;利用齿轮箱数据集分别从数据长度敏感性、算法特征提取时间、模型诊断性能三种角度对EHDE、精细复合多尺度样本熵、精细复合多尺度模糊熵、精细复合多尺度排列熵、精细复合多尺度散布熵、精细复合多尺度波动散布熵进行了对比研究。研究结果表明:EHDE方法对数据长度的要求较低,在数据长度为512时即可以取得99.1%的平均识别准确率,在诊断稳定性和诊断精度方面均优于其他对比方法;在算法的泛化性实验中,EHDE方法能够以98%的准确率识别齿轮箱的不同故障类型,具有明显的泛化性和通用性。