机床齿轮箱变速器振动特性分析及优化研究

针对机床在运行过程中需要平稳少振的特点,以某型号的机床齿轮箱变速器为研究对象,分析齿轮箱变速器振动的成因及理论减振方法。利用MASTA软件建立齿轮箱变速器仿真模型,对其进行齿轮耦合模态分析和阶次跟踪分析,得到关注频率内的共振参数和动态啮合力、啮合柔度相位及大小的变化曲线,确定变速器的谐波啮合阶次和响应峰值频率。通过游隙控制分析,获得游隙对齿轮箱变速器振动特性的影响规律,得到不同轴承对响应峰值的作用规律和不同游隙下各谐波阶次对应响应峰值的变化规律。结果表明：优化后的响应峰值显著降低,齿轮传递误差减小,改善了齿轮箱变速器的振动特性。     

神经网络在齿轮箱诊断中的应用

在认真学习了Elman网络的理论框架的基础上,总结分析了Elman神经网络用于故障诊断的不足,提出了改进的Elman神经网络,并将其应用于齿轮箱故障的故障诊断。建立了改进的Elman神经网络并进行仿真,经过一系列训练与测试,仿真结果表明：改进后的elman网络在稳定性及收敛速度等方面有明显优势,是可以在实际工程故障诊断中应用的一种非常有效的方法。     

基于互补的总体经验模式分解算法的齿轮箱故障诊断

针对经验模式分解(EMD)和总体经验模式分解(EEMD)算法在齿轮箱故障诊断中的缺陷,提出了一种基于互补的总体经验模式分解(CEEMD)算法的齿轮箱故障诊断方法。首先介绍了CEEMD方法,然后将CEEMD方法用于实际齿轮箱的故障诊断中。结果表明,与基于EMD/EEMD算法的齿轮箱故障诊断方法相比,基于CEEMD算法的齿轮箱故障诊断方法不但可以有效地克服模态混叠和能量泄露现象,而且大大提高了计算效率,为齿轮箱的故障诊断提供了一种新的方法。     

倒频谱分析在高速线材精轧传动箱故障诊断中的应用

倒频谱分析是故障诊断的一种有效手段,常用于从复杂的信号中提取周期信号.简要介绍了倒频谱的分析原理及特点,通过对现场工况的分析,运用时域、频域分析方法对高线精轧传动箱轴承故障进行诊断,并采用倒频谱方法从复杂谱线中提取出周期信号,对轴承故障做出判断,分析诊断结果与实际结果一致,验证倒频谱分析可提高轴承故障的准确性.     

基于自回归模型的齿轮轴破损诊断

齿轮轴失效引起的齿轮箱振动行为与轮齿失效引起的齿轮箱振动行为不同.传统的齿轮故障诊断方法大多针对于轮齿破损,难以有效识别齿轮轴破损.用自回归模型拟合正常齿轮振动的时域同步平均信号,利用Akaike判据获得自回归模型的阶数,用Levinson-Durbin递归算法求解Yule-Waker方程获得自回归模型的系数.将建立的自回归模型作为线性滤波器处理齿轮箱振动信号,获得预测误差信号.之后对预测误差信号进行两样本Kolmogorov-Smimov检验,获得正常齿轮轴振动信号和待处理齿轮轴振动信号预测误差的K-S统计距离和相似概率,并将其作为齿轮轴破损特征指标量.实际试验表明这一特征指标的有效性.     

Impact fault detection of gearbox based on variational mode decomposition and coupled underdamped stochastic resonance

In the gear fault diagnosis, the emergence of periodic impulse components in vibration signals is an important symptom of gear failure. However, heavy background noise makes it difficult to extract the weak periodic impulse features. Therefore, the paper presents an impact fault detection method of gearbox by combining variational mode decomposition (VMD) with coupled underdamped stochastic resonance (CUSR) to extract the periodic impulse features. First, the adaptive VMD is presented to decompose the vibration signal into several intrinsic mode functions (IMFs), which can automatically determine the appropriate mode number according to the correlation kurtosis (CK) of decomposition results and extract the sensitive IMF component containing the main fault information. Next, the adaptive CUSR method is developed to analyze the selected sensitive IMF component, and the optimal system parameters are obtained by the genetic algorithm using the CK index as optimization objective function. Finally, the periodic impulse features are extracted by the output signal of CUSR system accurately. Experiments and engineering application verify the effectiveness and superiority of the proposed adaptive VMD-CUSR method for extracting the periodic impulse features in gear fault diagnosis compared to other methods.     

LTSA和深度置信网络的行星齿轮箱故障诊断

针对行星齿轮箱振动信号维度高,传统故障诊断方法识别精度低的问题,提出一种基于局部切空间排列算法(Lo-cal Tangent Space Alignment,LTSA)和深度置信网络(Deep Belief Network,DBN)的行星齿轮箱故障诊断方法.首先,利用PCA算法预估高维数据的内在维度,确定目标数据的内在...     

Blind equalization using combined skewness–kurtosis criterion for gearbox vibration enhancement

In this paper a method for vibration signal enhancement is presented. It incorporates an idea that the signal acquired on the machine housing is a convolution of an informative signal (cyclic pulse train) with an impulse response of the system. The impulse response corresponds to a transmission path through which the informative signal propagates. The informative signal is a signal that contains information about a local damage. The classical method that estimates the impulse response of the system is called minimum entropy deconvolution (MED) and it aims to maximize kurtosis of the deconvolved signal, i.e. kurtosis of the informative signal estimate. Recently, skewness-based deconvolution (equalization) has been proposed as an alternative method for damage detection in rotating machines. In this paper we incorporate an alternative criterion which combines advantages of both of the previously used deconvolution criteria. Kurtosis is a widely-used tool for impulsiveness detection even if they are hidden in the signal, although favouring single-spike signals is a disadvantage of kurtosis. On the other hand, skewness is more robust, since it incorporates statistical moment one order lower than kurtosis. However, signals related to local damage are not always asymmetric, thus skewness is not a suitable criterion for their extraction. Thus, it is worth to combine both kurtosis and skewness in a single deconvolution criterion. We compare properties of two previously used criteria (kurtosis and skewness) with the novel one which is based on the Jarque–Bera statistic using a simulation study. An experimental validation on a real vibration signal (two-stage gearbox from an open-pit mine) is performed as well.     

链条炉排齿轮减速箱传动性能测试

采用机械封闭功率法，对输出轴转速板低而传动比和输出转矩很大的链条炉排齿轮减速箱的传动效率和牙嵌式安全离合器的弹簧压缩量与输出转矩关系进行了测定，为确定电动机的功率和牙嵌式安全离合器的弹簧压缩量提供了依据。试验方法简单，仪器设备投资小，测试准确，节能，取得了较好的效果。     

基于模态动能法的行星变速箱测点优化

由于某型行星变速箱结构较为复杂,故障检测和状态监测时振动传感器的安装位置往往凭经验确定,为使传感器安装位置更科学、更合理,利用模态动能法对行星变速箱测点进行选择。针对变速箱箱体结构建立了三维模型,利用ANSYS软件进行了有限元仿真分析,得到各阶模态、固有频率和振型,导出低阶振型并利用模态动能法计算箱体表面的模态动能值,初选箱体表面六个测点,从中选取四个模态动能值最大的作为最终测点。设计试验方案,获取了箱体表面六个初选测点的振动信号,计算得到各测点实测信号的均方根、均值三个指标对测点的敏感度进行评价,试验验证了测点选择的科学性,为变速箱状态监测打下基础。