机械转子动静碰摩故障的EMD分解Hilbert包络谱分析

为避免碰摩故障对旋转机械的影响,针对转子系统局部碰摩的特征,提出一种基于EMD分解Hilbert包络谱分析方法。该珐利用EMD方法分解含有碰摩故障的振动信号,提取出的IMF分量有明显的调幅特征,再对其中突出的IMF分量进行Hilbert包络谱分析提取出故障特征频率。与倒谱分析相比,得到的碰摩故障信息更加精确;与小波分析相比,能更容易提取出真实的故障特征。     

Compound rub malfunctions feature extraction based on full-spectrum cascade analysis and SVM

The acceleration signals of operational rotor vibration provide a lot of information about its running behaviour. The acceleration signal features of identification and extraction in the process of speed change are important for the fault diagnosis of rotating machinery. The full-spectrum cascade analysis of rotating machinery vibrations is an efficient method that enables the symptoms of some special types of fault (especially for rub) to be clearly detected. Some typical compound rub malfunctions have been researched by experiments in this study. Acceleration signals have been received by the experimental apparatus and analysed by full spectrum. The abrupt changes in surging acceleration signals of rotor malfunctions can be detected and their fault feature spectra shown in full-spectrum cascade plots. The full-spectrum experimental data are applied to the support vector machine (SVM) training to be classified. The results indicate the potential and feasibility of this approach for the diagnosis of rotor malfunctions. The full-spectrum cascade plot can enhance the feature information for the knowledge base of the rotating machinery rub fault diagnosis system and is of great significance to diagnose compound rub faults in a rotor more accurately.     

On the contact of partial rotor rub with experimental observations

Partial rotor rub occurs when an obstacle on the stator of a rotating machinery disturbs the free whirling motion of the rotor, which is more common than full annular rub among the cases of rubbing in rotating machinery. The intermittent contacts and friction during partial rotor rub makes the phenomenon complex. The several nonlinear phenomena of superharmonics, subharmonics, and jump phenomenon are demonstrated for the partial rub using an experimental apparatus in this study. The orbit patterns are also measured experimentally. In order to explain the phenomena of partial rotor rub, the analytical model for the contact between the rotor and stator should be chosen carefully. In this respect, a piecewise-linear model and a rebound model using the coefficient of restitution are investigated on the basis of the experimental observations. Also, Numerical simulations for the two models of contact are done for the various system parameters of clearance, contact stiffness, and friction coefficient. The results show that the piecewise-linear model for partial rotor rub is more plausible to explain the experimental observations.     

松动对碰摩转子-轴承系统非线性特性的影响研究

在同时考虑轴承油膜力和碰摩发生时转子与定子之间的相对滑动速度对非线性摩擦力的影响基础上,构造了含有松动和碰摩故障转子系统的动力学模型,对转子 轴承系统由松动和碰摩耦合故障导致的非线性动力学行为进行了数值仿真研究,发现该类系统在运行过程中存在周期运动、拟周期运动和混沌运动等丰富的非线性现象,研究结果为转子 轴承系统故障诊断、动态设计和安全运行提供了理论参考。     

基于支持向量机的旋转机械故障诊断

把支持向量机应用于诊断旋转机械不平衡和转静碰摩故障，利用转子故障实验器分别对多项式和径向基核函数进行了实验比较，选取了不同振动参数作为特征量输入支持向量机进行学习和测试。结果表明．两种不同核函数的支持向量机在各种条件下所获得的最优故障诊断准确率很接近。这说明支持向量机的性能对结构（核函数）的依赖性很小，便于在工程中应用，但特征量的选取对故障诊断准确率影响很大。对于诊断不平衡和转静碰摩故障．一、二和三阶正、反进动量是最适合的故障诊断特征量。用正、反进动量构造出SV-进动图，可明确、形象地显示故障分类面，有助于诊断故障。     

基于解析模态分解的机械故障诊断方法

针对旋转机械故障诊断问题,提出了一种基于解析模态分解(AMD)的旋转机械故障诊断方法。只要知道信号的频率成分,AMD方法就可以将含不同频率成分的信号分解为单频率信号,尤其能够分解有紧密间隔频率成分的信号。对于可预知故障特征频率的旋转机械的故障诊断,可利用AMD方法提取机械振动信号中故障特征频率所在频段的信号,并求该段信号的频谱,若频谱中含有故障特征频率,则说明机械振动信号中存在该故障。通过对滚动轴承故障信号和转子不对中故障信号的分析以及和经验模态分解(EMD)方法的对比,证明了AMD方法的有效性,且AMD方法比EMD方法更快速、准确。     

基于全矢谱的旋转机械回转相位及应用研究

研究了旋转机械故障诊断中转子截面相互垂直两个同频信号的相位差信息,揭示了相互垂直两个信号的不同相位差对应不同的合成运动轨迹图形,推导出全矢谱分析理论中初相位的角度大小,并由初相位绘制出双截面、多截面转子的空间振形,最后通过实例将全矢谱理论结合转子空间振形和初始相位差信息应用到旋转机械不平衡故障分类中。结果表明:由初相位绘制的空间振形图可以有效地区分旋转机械不平衡故障中发生的具体形式,提高了故障诊断的准确性。     

基于声信号三维谱分析的转子故障特征提取的实验研究

对转子的一些典型故障（不平衡、松动、碰磨、不对中以及突加不平衡）进行试验模拟,对故障发生过程中的声信号采用声望话筒和十六通道索尼磁带记录仪进行采集的基础上,分析转子故障声信号的三维谱特性,提取和总结出由故障造成的声信号突变和对应的声谱特征。分析结果表明,采用声信号对转子的一些故障进行诊断是可行的。     

松动—碰摩耦合故障转子系统动力学特性分析

基于有限元法,建立考虑松动、碰摩及松动—碰摩耦合故障的转子—轴承系统的动力学模型,其中松动故障采用分段线性刚度和阻尼模型,转定子碰摩采用点—点接触模型。通过增广的拉格朗日方法来处理接触约束条件,修订的库仑摩擦模型来模拟转定子之间摩擦。考虑不同转速对松动、碰摩及松动—碰摩转子系统动力学特性的影响,并对比三者之间的异同点。研究表明,碰摩在耦合故障中处于主导地位,而松动主要影响松动端局部振动处于从属地位;松动—碰摩耦合故障响应在低转速和高转速与单一碰摩故障类似,在中间转速存在一些差别,但总体运动趋势基本一致;与单一松动故障相比发现,碰摩能够减小松动引起的低频振动,主要激发高频振动。研究结果可为转子系统耦合碰摩故障诊断提供依据。     

Rotating machinery diagnosis using wavelet packets-fractal technology and neural networks

This paper presents a new fault diagnosis procedure for rotating machinery using the wavelet packets-fractal technology and a radial basis function neural network. The main purpose is to investigate different fault conditions for rotating machinery, such as imbalance, misalignment, base looseness and combination of imbalance and misalignment. In this study, we measured the non-stationary vibration signals induced by these fault conditions. Applying wavelet packets transform to these signals, the fractal dimension of each frequency channel was extracted and the box counting dimension was used to depict the failure characteristics of the fault conditions. The failure modes were then identified by a radial basis function neural network. An experiment was conducted and the results showed that the proposed method can detect and recognize different kinds of fault conditions. Therefore, it is concluded that the combination of wavelet packets-fractal technology and neural networks can provide an effective method to diagnose fault conditions of rotating machinery.     

松动-碰摩耦合故障转子系统振动特性分析

针对动态油膜力下带有支座松动 碰摩耦合故障的转子 轴承系统的非线性动力学模型,采用数值模拟等方 法,考察其随转速比变化及改变最大松动间隙值时发生松动和碰摩故障的分岔与混沌特性,并分别与相同参数条件 下带有支座松动故障的转子系统和碰摩故障转子系统进行比较,得到如下结论:该耦合故障系统中碰摩与松动故障 主要发生在主共振区、油膜振荡区及转速比大于3.4的转速范围内,发生碰摩故障时系统运动多为混沌运动;随着 松动最大间隙值的减小,碰摩的转速范围越来越大,而发生松动的转速范围越来越小;两种故障的产生相对独立。     

基于转子实验台的典型故障信号模拟与分析

介绍了一种通过转子实验台模拟转子不平衡和摩擦两种故障来获取该典型故障信号的方法,并对信号的产生进行了机理分析,给出了典型信号在正常和故障两种不同情况下的时域和频域分析图。将实验结果与理论分析相结合,验证了旋转机械两种典型振动故障的识别特征,丰富了故障档案。     

南昌钢铁厂轴流压缩机振动故障分析

介绍了南昌钢铁厂新安装的一台轴流压缩机机组振动测试的情况，深入分析了机组故障原因，制定了故障治理方案，实施后取得了良好效果。     

双相干谱和RBF网络在旋转机械故障诊断中的应用

双相干谱保留了信号的相位信息，可以用来描述非线性相位的耦合，径向基函数网络具有良好的推广能力和分类能力。文中将双相干谱和径向基函数网络结合，提出一种基于双相干谱与径向基函数网络相结合的旋转机械故障诊断方法，即以双相干谱为故障特征向量，以径向基函数网络作为分类器，对旋转机械的故障进行分类，并以转子不平衡、转轴碰摩、油膜涡动为例进行实验研究。实验结果表明，结合双相干谱和径向基函数网络的旋转机械故障诊断方法是有效的。     

The processing of rotor startup signals based on empirical mode decomposition

In this paper, we applied empirical mode decomposition method to analyse rotor startup signals, which are non-stationary and contain a lot of additional information other than that from its stationary running signals. The methodology developed in this paper decomposes the original startup signals into intrinsic oscillation modes or intrinsic modes function (IMFs). Then, we obtained rotating frequency components for Bode diagrams plot by corresponding IMFs, according to the characteristics of rotor system. The method can obtain precise critical speed without complex hardware support. The low-frequency components were extracted from these IMFs in vertical and horizontal directions. Utilising these components, we constructed a drift locus of rotor revolution centre, which provides some significant information to fault diagnosis of rotating machinery. Also, we proved that empirical mode decomposition method is more precise than Fourier filter for the extraction of low-frequency component.     

基于模糊神经网络的高阶频段振动信号故障诊断研究

将模糊神经网络技术应用于机械故障高阶频率振动信号的研究,参考齿轮和转子故障模式并结合专家经验建立了螺杆压缩机转子故障诊断专家系统知识库,利用振动频谱特征就螺杆压缩机的几种故障模式结合一种模糊神经网络故障诊断模型进行了模糊神经网络识别。算例诊断结果为压缩机阴阳转子型线加工误差,与试验结果一致。     

谐波小波的时频特性分析及其在故障诊断中的应用

小波分析在故障诊断中得到较广泛的应用,但采用不同的小波,分析结果往往会有很大差异。本文通过对谐波小波的选频和相位特性的理论分析,结合仿真信号与故障试验进行分析研究,提出了将基于谐波的小波包分析方法用于诊断碰摩故障,并提出了基于谐波小波的进化谱分析方法,为松动故障的识别提供了一种有效手段。     

转子启动信号处理中的低频分量提取研究

针对传统滤波方法处理非平稳信号的不足,提出利用经验模态分解法来处理转子启动信号,通过此方法的自适应滤波特性来提取这类信号中的低频分量。首先,用模拟信号对此方法进行验证。然后,应用于实际转子启动信号的处理中。结果表明,隐含在信号中的低频分量被成功地提取出来。根据转子系统的特性进行分析,指出此低频分量实质上是转子公转时所围绕中心点的漂移轨迹,其幅值必须在一定的范围内,否则转子系统可能存在故障。因此,该物理量的提取对转子系统的故障诊断有重要作用。     

基于RO-SBM的Hilbert Huang变换端点

针对Hilbert-Huang变换方法中由于信号经验模态分解(empirical mode decomposition, 简称EMD)过程中所存在的端点效应问题,分析了现有数据延拓方式的利弊,并在基于斜率(slope based method, 简称SBM)方法以及改进方法(improved slope based method,简称ISBM)的基础上提出了一种全新的基于斜率再优化(re-optimization slope based method, 简称RO-SBM)方法用于信号序列的极值点延拓,然后对延拓后的数据进行EMD分解,得到相应的 本征模函数(intrinsic mode function,简称IMF)分量。数值仿真结果表明,采用基于RO-SBM方法进行数据延拓,相比镜像延拓以及ISBM方法,可以更有效地抑制EMD中的端点效应问题, 提升HHT方法的信号分析性能。通过基于RO-SBM方法进行数据延拓的HHT方法准确分离出了某转子系统的局部碰摩径向振动信号中所包含的故障特征分量,并将此方法成功应用于旋 转机械故障诊断领域。     

基于基座多振动信号融合的转子故障诊断

针对目前常用的旋转机械转子故障诊断中直接对转子部件诊断安装传感器困难、通用性差等问题,利用转子-轴承-基座系统的振动传递性,提出了一种基于基座的多传感器信息融合故障诊断方法。采用相关函数法来确定基座上传感器的最佳布置方案,通过自适应的信号融合方法,克服基座上故障信号特征较弱,包含更多干扰信号的特点,对转子部件故障进行有效诊断。最后在综合故障试验台中进行试验验证,证明了该方法的可行性。