多级行星齿轮系统耦合动力学分析与试验研究

基于齿轮啮合理论和Lagrange方程,考虑各级齿圈扭转支撑刚度,提出运用集中参数法建立多级行星齿轮—箱体耦合扭转动力学模型。在分析多级行星齿轮啮合相位关系的基础上,确定各齿轮对时变啮合刚度,并运用有限元法获取各齿圈扭转支撑刚度。行星齿轮传动误差表示为轴频和齿频叠加的谐波函数,分析多级行星齿轮传动的主要激励特征。针对盾构机三级行星减速器某施工地段的运行条件,求解行星齿轮系统的动态响应,并分析其时频特性。采用背靠背能量回馈试验台架测试方案,测量盾构机行星减速器的振动加速度,采用数值积分计算振动速度和位移,并分析其振动特性。研究表明盾构机行星减速器振动试验数据与计算结果具有良好的一致性,验证多级行星齿轮系统耦合动力学模型的准确性。     

四级行星齿轮减速器耦合系统动态性能优化

为研究复杂激励作用下多级行星齿轮减速器的耦合振动响应及动态性能优化方法,以某型海洋平台升降齿轮箱为对象,建立了包含时变啮合刚度、传动误差、啮合阻尼、齿侧间隙、摩擦力矩、结合部刚度和阻尼等因素的四级行星耦合系统动力学集中参数模型,采用龙格库塔法对额定工况下的动力学方程进行求解,并将计算所得的振动响应与实测结果进行对比,两者吻合较好。在此基础上,提出了基于谐波平衡法的多级行星齿轮耦合系统动态优化方法,以减速器壳体尺寸参数和齿轮副基本参数为设计变量,减速器总传动比、实际中心距、轮齿强度等为约束条件,振动加速度均方根值和总体质量最小为目标,建立多目标混合离散优化模型,基于分枝定界算法编写程序求解模型,获得最优设计变量。结果表明,优化后减速器总质量减小8.2%,传动系统振动加速度降低33.4%,壳体振动加速度降低70.5%,优化效果明显。     

参数优化形态谱和SVM的行星齿轮箱故障诊断

针对行星齿轮箱的故障诊断问题,提出一种参数优化数学形态谱和支持向量机的行星齿轮箱故障识别方法.该方法考虑形态谱参数选择对同型故障一致性和不同故障差异性的影响,通过相对误差指标值的大小选择形态谱最优参数,对形态谱参数进行优化选择,提取故障特征,并采用支持向量机完成行星齿轮箱故障的故障识别.相较传统行星齿轮箱故障诊断而言,该方法对形态谱参数进行了定量分析,且无需复杂数学建模,和频率成分分析,简化故障识别过程.为了验证该方法的有效性,对行星齿轮箱试验台信号进行了分析实验,结果表明了该方法可有效地识别齿轮故障类型.     

漫谈齿轮减速器

齿轮减速器是和其他驱动配套装置联接的闭式齿轮传动装置。虽然减速器有时也被称为“齿轮箱”,但这个名称通常是指装在一台机器内部的齿轮传动系。不少减速器的最大转速可达3600转/分。一般来说,减速器不同时用来作为增速器。一般用途的中型和小型的齿轮减速器,绝大部分采用了斜齿轮、锥齿轮、行星齿轮和蜗     

基于注意力机制的一维卷积神经网络行星齿轮箱故障诊断

针对行星齿轮箱故障信号成分复杂和时变性强的特点,提出了基于注意力机制的一维卷积神经网络(１D-CNN )行星齿轮箱故障诊断方法.首先,将行星齿轮箱各类故障状态的原始振动信号进行分段处理,作为模型的输入;其次,利用一维卷积神经网络对行星齿轮箱的原始振动信号学习齿轮故障特征,结合注意力机制( AM )对特征序列自适应的赋予不同的权重,增强故障特征信息;最后,利用 Softmax 分类器实现行星齿轮箱的故障诊断.通过故障实验验证以及与其他模型的对比,该故障诊断模型具有较强的学习能力,诊断性能优于其他的深度学习模型,有较好的工程实际意义.     

某核电海水循环泵齿轮箱振动分析

齿轮的边频带,通常作为齿轮故障诊断的重要依据,针对某核电海水循环泵齿轮箱首次满载振动偏高,基于边频带理论对齿轮箱振动频谱进行了分析,分析结果表明该齿轮箱振动频谱符合无故障行星齿轮组的典型特征,可以排除齿轮箱局部故障的可能,该齿轮箱满足长期稳定运行的要求。     

矿用带式输送机减速器故障诊断的研究

针对煤矿带式输送输送机齿轮减速器故障诊断方法在实践应用过程中存在的不足,提出了采用矿用便携式测振记录仪基于振动数据监测的煤矿井下带式输送机齿轮减速器的故障诊断方法。将该方法在虎龙沟煤业有限公司81505工作面DSJ1200带式输送机上的SSX200-18A行星减速器进行了试验实践,通过实践表明该速器的故障诊断方法是可行有效的,具有较好的市场推广价值。     

基于局域均值分解的行星齿轮箱故障诊断方法

在低转速工况下,容易出现行星齿轮箱故障的微弱信号和强信号难以分离的情况,导致行星齿轮箱存在微弱故障诊断精度较差的问题,为此,提出了一种基于局域均值分解(LMD)的行星齿轮箱故障诊断方法。首先,采用DASP数据采集系统,采集了行星齿轮箱不同工况下的振动信号,采用平移不变量小波降噪方法,对其振动信号进行了降噪处理;然后,采用局域均值分解方法分解了其振动信号,分别采用了能量算子和循环频率对其进行了解调处理,获取了微弱故障信号分量所对应的幅值和相位调制信息,准确提取了行星齿轮箱的微弱故障信号特征;最后,采用最小二乘支持向量机(LSSVM)识别了齿轮箱不同故障特征,判断了行星齿轮箱的运行状态,实现了行星齿轮箱的故障诊断。研究结果表明：采用基于LMD的方法,可以对行星齿轮箱的微弱异常信号及强异常信号进行准确诊断,获得满意的行星齿轮箱故障诊断结果,有效保障行星齿轮箱的安全、稳定运转。     

基于集成卷积神经网络的行星齿轮智能诊断方法

针对行星齿轮箱故障振动特征需要预处理、识别困难以及诊断模型收敛速度较慢的问题,提出基于集成卷积神经网络的行星齿轮箱智能故障诊断方法。首先,采用一维卷积对齿轮的原始时域振动信号提取特征,之后通过采用两个弱分类器,根据弱分类学习错误率的性能更新样本权重,调整权重后根据训练集训练弱分类器。重复此过程,最后通过设置策略整合弱分类器,形成集成卷积神经网络;建立一个稳定用于行星齿轮箱的智能故障诊断的模型。实验结果表明：集成卷积神经网络能很好地对行星齿轮原始振动信号进行快速诊断。相对于传统卷积神经网络对齿轮原始时域振动故障信号的诊断具有更强的辨识能力和更快的收敛速度;所建立的智能诊断模型可以有效地诊断齿轮不同的故障状态。     

基于神经网络的齿轮故障诊断专家系统

基于齿轮典型故障机理及其信号特征,采用时域、幅值域与频谱分析相结合的诊断方法,建立了齿轮故障诊断神经网络模型,试验验证模型诊断结果具有较高准确性。基于Windows平台和Visual C＋＋语言,开发了基于神经网络的齿轮故障诊断专家系统,将传统的时频信号分析理论与现代小波分析、神经网络和专家系统技术融入齿轮故障诊断之中,形成一个更加完善基于神经网络的齿轮故障诊断专家系统。     

基于电机电流经验模态分解的行星轮故障诊断

在故障诊断领域,电机电流信号分析法(MCSA)已经逐渐应用于齿轮故障诊断中,但该方法在诊断行星轮缺齿故障时由于电流基频干扰较大,导致故障特征不明显,难以实现故障诊断。因此提出一种基于电流信号经验模态分解(EMD)的故障诊断方法。通过对电机电流信号进行EMD分解,选取合适的IMF分量经傅立叶变换求其频谱图,根据频谱图中是否存在与故障特征频率相关的频率,实现了对行星轮缺齿故障的有效诊断。并通过实验分析,验证了该方法的有效性。     

Study on planetary gear fault diagnosis based on entropy feature fusion of ensemble empirical mode decomposition

Because planetary gear is characterized by its small size, light weight and large transmission ratio, it is widely used in large-scale, low-speed and heavy-duty mechanical systems. Therefore, the fault diagnosis of planetary gear is a key to ensure the safe and reliable operation of such mechanical equipment. A fault diagnosis method of planetary gear based on the entropy feature fusion of ensemble empirical mode decomposition (EEMD) is proposed. The intrinsic mode functions (IMFs) with small modal aliasing are obtained by EEMD, and the original feature set is composed of various entropy features of each IMF. To address the insensitive features in the original feature set and the excessive feature dimension, kernel principal component analysis (KPCA) is used to process the original feature set. Kernel principal component extraction and feature dimension reduction are performed. The fault diagnosis of planetary gear is eventually realized by applying the extracted kernel principal components and learning vector quantization (LVQ) neural network. The experiments under different operation conditions are carried out, and the experimental results indicate that the proposed method is capable of extracting the sensitive features and recognizing the fault statuses. The overall recognition rate reaches to 96% when the motor output frequency is 45 Hz and the load is 13.5 N m, and the fault recognition rates of the normal gear, the gear with one missing tooth and the broken gear can reach to 100%. The recognition rates of different fault gears under other operation conditions also can achieve better results. Thus, the proposed method is effective for the diagnosis of planetary gear faults.     

风机行星齿轮系统齿轮裂纹故障诊断

针对风力发电机实际行星齿轮系统,由于幅值及相位调制现象(各种制造误差不可避免等原因所导致)带来的故障诊断难题,搭建了含各种制造误差的动力学模型。模型考虑了出现裂纹故障以后,故障对时变啮合刚度以及传递误差的影响,通过数值求解,对比行星轮、太阳轮以及齿圈出现故障后与正常齿轮系统的包络谱结构特性,总结了故障特征频率。在风力发电机齿轮箱实验台上进行裂纹故障试验验证,结果表明所总结的故障特征频率可以作为风力发电机裂纹故障诊断及定位的依据。     

Health condition identification of planetary gearboxes based on variational mode decomposition and generalized composite multi-scale symbolic dynamic entropy

This paper proposes a novel fault diagnosis method based on variational mode decomposition (VMD) and generalized composite multi-scale symbol dynamic entropy (GCMSDE) to identify the different health conditions of planetary gearboxes. First, VMD is adopted to remove the noises and highlight the fault symptoms. Second, GCMSDE is utilized to extract the fault features from the denoised vibration signals. Third, the Laplacian score (LS) approach is employed to refine the fault features. Finally, the new features are fed into Softmax regression to identify the health conditions of planetary gearboxes. The proposed method is numerically and experimentally demonstrated to be able to differentiate seven localized fault types on the sun gear, planet gear and ring gear of planetary gearboxes.     

变速行星齿轮系统故障诊断方法

针对风力发电机行星齿轮系统变速非平稳工况,且故障信号耦合调制严重、传递路径复杂、噪声污染严重等特点,提出了基于阶次包络分析的故障诊断方法,详细阐述了方法原理和实现流程。通过对变速工况下的仿真加速度信号进行阶次包络分析,对比行星轮、太阳轮以及齿圈出现故障后与正常齿轮系统的阶次包络谱结构特性,总结了不同部件故障的特征阶次。在此基础上,通过对变速行星齿轮系统试验信号的分析表明:阶次包络分析方法能较好地抑制噪声干扰,反映故障特征清楚,且故障特征能作为变转速时行星齿轮系统故障诊断和定位的依据。     

Fault identification method for planetary gear based on DT-CWT threshold denoising and LE

Planetary gear is widely used in large-scale complex mechanical systems. However, because of the particularity of planetary gear transmission, serious wear and fatigue crack failures often occur in the sun gear, planet gears, and inner gear ring. In addition, every type of fault will experience different degradation processes. Improving the operation reliability of mechanical equipment through fault diagnosis of planetary gears and monitoring their degradation process is beneficial. This paper proposes a planetary gear fault identification method based on Dual-tree Complex wavelet transform (DT-CWT) threshold denoising and Laplacian eigenmaps (LE). The noise reduction processing of the original signal is achieved by the DT-CWT threshold denoising method, which takes full advantage of DT-CWT and is combined with the wavelet threshold of rigrsure principle. The original high-dimensional feature set, including the time domain features, frequency domain features, permutation entropy, and fractal box dimension of the denoised signal, is constructed from multi-angles. To solve the problems of excessively large feature dimension and the existence of redundant information, the LE algorithm is used to reduce the dimension of the original high-dimensional feature set, and the low-dimensional sensitive features are obtained. Through the above method, the effective identification of different fault states and different degradation states of the planetary gear are achieved.     

行星轮系转矩大幅波动故障特征及诊断方法

应用运动学和动力学理论对内齿轮不平衡、内齿轮与太阳轮回转中心不重合、主动齿轮不平衡等故障进行分析，得出行星轮系统驱动电机电流大幅度波动的特征频率和机械振动的特征频率。齿轮箱机械振动和驱动电机电流波动特征频率联合可以有效诊断行星轮轨道宽度变化致使电流大幅波动的故障。以某冶金厂大型重载齿轮箱为例，详细阐述诊断方法的应用，并证明诊断方法的有效性。     

行星齿轮典型断齿故障的动力学仿真

基于一种改进的行星齿轮箱集总参数模型,将断齿故障等效到时变啮合刚度中,建立了直齿行星齿轮的太阳轮断齿故障、行星轮断齿故障和内齿圈断齿故障的动力学模型。以某单级行星齿轮为研究对象,考虑扭转方向外部激励,对其正常和各断齿故障状态下的动力学模型进行求解,对内齿圈垂直振动加速度信号进行分析。研究结果表明:只考虑扭转方向的外部激励时,正常状态下内齿圈最高点处的垂直振动加速度信号频谱中幅值最高的共振峰与模态能量分布相关;在各断齿故障状态下,内齿圈垂直振动加速度的有效值均大于正常值,时域波形中可看到不同形式的冲击,包络谱中可看到对应的故障特征频率及其倍频成分。     

Model-based analysis and fault diagnosis of a compound planetary gear set with damaged sun gear

The vibration properties of compound planetary gears are more complicated than that of simple ones. This paper aims to investigate the fault properties of a compound planetary gear set in chipped sun gear conditions using model-based method. A three-dimensional lumped-parameter nonlinear dynamic model for the compound planetary gear set is established. This model considers the time-varying mesh stiffness (TVMS), the mesh phase relations, and gear chipping defects. The analytical equations are derived to quantify the TVMS reduction induced by the chipped gear based on the improved potential energy method. Further, the simulations are performed to demonstrate the fault features of sun gears with single or multiple chipped teeth in different gear stages. Moreover, the theoretical derivations are validated through the experimental signals analysis.     

基于自混合干涉的齿轮箱故障诊断技术

将激光自混合干涉（SMI）技术用于齿轮箱的故障检测,设计出一种新的齿轮箱故障检测传感器。采用QL65D5SA型半导体激光自混合传感器、冯哈勃2342l012CR空心杯减速电机自带的行星齿轮箱,搭建了行星齿轮箱故障SMI检测系统,并对行星轮Z1做断齿故障实验。通过对时域波形的分析,可以找到额定转频下的12个冲击点;通过对齿轮箱故障信号傅里叶频谱的分析,发现故障齿轮的啮合频率周围出现与故障齿轮特征频率和行星架转频呈整数倍关系的边带,且啮合频率处的波形幅值明显增大,这些都与齿轮副的理论振动模型相符合。