基于虚拟仪器的转子轴心运动轨迹测试系统

针对旋转机械故障的特征,运用电涡流传感器和虚拟仪器构成轴心运动轨迹测试系统,在转轴同一横截面上安装2个相互垂直的电涡流位移传感器,同时采集2个传感器传入的水平和垂直信号;将2路信号叠加并在XY Graph控件显示出转子轴心运动轨迹图。利用故障的特征信息判断旋转机械转子运行状态和进行故障诊断。     

旋转机械空间域全信息小波包变换及其应用

结合空间域矢谱分析和小波包变换技术,提出了旋转机械空间域全信息小波包变换方法.它融合了转子同截面3通道图谱中各自存在的振动分量,能全面、准确地反映转子发生故障时的瞬态过程特征量变化.工程实践应用表明:旋转机械空间域全信息小波包变换对于旋转机械的故障诊断,是一种新的、较为实用的信息融合方法.     

基于SDP图像与VGG网络的旋转机械转子故障诊断研究

针对传统故障诊断方法对旋转机械转子故障状态识别精度较低的问题,提出了一种基于对称点模式图像特征信息融合与深度学习相结合的旋转机械转子故障诊断方法。采用SDP信息融合技术,对转子故障状态下的多通道振动信号进行了信息融合,通过SDP图形特征可简单直观地区分不同转子故障振动状态;结合深度学习VGG网络自适应提取了SDP图像的特征信息,对不同故障转化的SDP图像实现了准确的诊断识别,进而判别了其故障类型;通过变速器机械故障模拟实验验证了所提出方法的有效性,并与传统机器学习方法极限学习机(ELM)进行了比较。研究结果表明:基于SDP图像与VGG网络的旋转机械转子故障诊断方法解决了转子故障振动信号中存在的高复杂、非线性和不稳定问题,与传统机器学习方法ELM相比具有更高的识别精度。     

基于盲源分离与小波降噪的旋转机械故障分析

基于小波降噪和盲源分离相结合对机械信号进行分离与故障诊断。首先使用经分析选择的较好小波阈值对非平稳振动信号进行降噪,然后运用盲源分离技术分离出激振信号,结果表明利用小波阀值降噪后进行盲源分离时分离信号与源信号相似系数优于直接盲源分离;将小波降噪和盲源分离相结合应用于某燃气轮机的实测故障信号提取,诊断出转子发生了不平衡及碰摩等故障现象,与实测情况相符,有效说明了该方法在旋转机械故障诊断中的实用性。     

现场设备诊断技术讲座第四讲 旋转机械分类诊断及常见故障判别

一、旋转机械的特点及分类诊断 旋转机械的转子系统,包括转轴及零部件。按转子的工作转速分为柔性转子和刚性转子。前者运行转速高于转子本身一阶自娠频率(即固有频率),后者运行转速低于一阶自振频率。旋转机械产生的振动信号大多是一些周期信号、准周期信号或平衡随机信号。旋转机械的故障特征频率都与转子的转速(或转频)有关,是转子的回转频率及其倍频、分频。因此,重视振     

基于DSP和BP神经网络的旋转机械在线故障诊断系统

为了满足旋转机械在线故障诊断的需要,设计了基于DSP和BP神经网络的在线故障诊断系统,采用DSP芯片TMS320F2812作为主控芯片,开发了具有振动信号采集、转速测量、输入输出的硬件系统.针对旋转机械的常见故障,开发了基于DSP的频谱分析软件,并将BP神经网络嵌入DSP中实时运行以实现旋转机械的自动故障诊断.文中使用该系统对旋转机械的转子不平衡、不对中,轴承外圈损坏、断齿、轴承座松动5种故障进行了实测,结果表明它能正确地识别出故障的类型.使用该系统进行旋转机械在线故障诊断可以提高生产效率,保证设备的长时间稳定运行.     

全息动平衡相位补偿方法

全息动平衡法融合转子系统测振截面中两个相互垂直方向的振动参量制定系统的配重方案,以保证各测振截面周向各方向均具有良好的振动;然而全息动平衡法以初相矢为平衡目标制定的配重方案有时会出现不能保证测振截面周向振动的情况。通过各向异性转子系统失衡和响应关系的分析研究,发现初相矢与转子系统的失衡量之间存在线性映射关系;在原始失衡与配重截面存在轴向位置差异的条件下,受系统降维等效的影响,失衡和响应"椭圆映射"关系中的时间耦合问题是影响初相矢平衡方案不可靠的一个主要原因。在以上分析的基础上,提出一种相位补偿方法修正并优选初相矢对应的配重方案,以消除时时间耦合问题的影响;通过仿真试验,现场案例分析,验证了相位补偿方法对应配重方案的可靠性。     

基于Matlab的转子典型故障诊断与分析

以旋转机械为研究对象,采用Matlab软件平台,通过绘制转子在不平衡、摩擦典型故障时的轴心轨迹图方法,监测和分析旋转机械的振动故障。采用平均法提纯了轴心轨迹图,减小了随机噪声的影响,还原出干净的轴心轨迹图。试验表明,采用Matlab方法不会造成数据量的减少及细节信号的丢失,能较好地诊断转子的振动故障。     

旋转机械故障的双相干谱特征及其识别

提出了基于双谱分析的旋转机械故障诊断新方法。利用双相干谱函数提取机械振动信号中由故障引起的非线性相位耦合,并且加以分析。通过对几种典型的旋转机械故障分析表明,双相干谱不仅能敏感地监测故障的出现,而且可以有效地识别各种不同的故障模式。     

齿轮传动链故障监测系统的信号分析和研究

研究了故障监测系统和故障信息的调制和解调,根据有限长度调制信号的初相位,相位差及相位调制量,对机械故障进行实时监测,针对齿轮箱故障诊断和相应监测系统的设计应用,实现了远程对调相信号的解调和相位调制信号的提取,解决了PLC和微机齿轮传动链故障监测系统的数据监测,系统抗干扰及数据通讯中的一些问题。     

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。     

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.     

基于小波包的全信息解调方法及其应用

针对旋转机械故障信号的振动特点,将小波包络解调与基于数据融合技术的全矢谱相结合,提出一种诊断旋转机械调制信号的分析方法。首先,对安装在转子同一截面不同方向上的传感器信息同步整周期采样,对来自不同方向的时域信号分别采用小波包进行分解并重构,以实现带通滤波的效果;然后,采用全矢谱技术对两组重构信号进行数据融合;最后,对合成后的信号做包络解调分析。通过仿真研究和工程实例分析可以得出,对来自同一截面、不同方向的时域信号分别作小波包络谱分析时,两者在能量分布和频谱结构上存在着较大差别,以致造成提取故障信息的不完整或造成误判、漏判。基于小波包的全信息解调分析方法通过对同源的双通道信号的有效融合,可全面地反映出信号中包含的不同调制信息。与基于全矢谱的传统包络解调分析进行对比分析,具有较好的分析结果和可信度。     

基于频域时空特征谱的信息故障诊断方法

目前振动信号的分析主要是针对特定测点在某一瞬间采集的一段振动波形,提取其中的特征量来进行诊断故障,这种基于状态信息的诊断方法对故障类型的辨别能力有限。基于多个振动波形状态的过程信息,提出和定义了两种基于过程信息融合的信息火用指标,用于反映同一个过程中不同状态间的过程变化规律以及不同过程中对应状态间的过程变化规律。在此基础上,提出了一种基于频域时空特征谱的旋转机械信息火用故障诊断方法,并通过该方法对试验台获取的振动故障信号进行分析。计算结果表明,该方法是一种有效的故障诊断方法。     

New method of fault diagnosis of rotating machinery based on distance of information entropy

This paper introduces the basic conception of information fusion and some fusion diagnosis methods commonly used nowadays in rotating machinery. From the thought of the information fusion, a new quantitative feature index monitoring and diagnosing the vibration fault of rotating machinery, which is called distance of information entropy, is put forward on the basis of the singular spectrum entropy in time domain, power spectrum entropy in frequency domain, wavelet energy spectrum entropy, and wavelet space feature entropy in time-frequency domain. The mathematic deduction suggests that the conception of distance of information entropy is accordant with the maximum subordination principle in the fuzzy theory. Through calculation it has been proved that this method can effectively distinguish different fault types. Then, the accuracy of rotor fault diagnosis can be improved through the curve chart of the distance of information entropy at multi-speed.     

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.     

Multiscale singular value manifold for rotating machinery fault diagnosis

Time-frequency distribution of vibration signal can be considered as an image that contains more information than signal in time domain. Manifold learning is a novel theory for image recognition that can be also applied to rotating machinery fault pattern recognition based on time-frequency distributions. However, the vibration signal of rotating machinery in fault condition contains cyclical transient impulses with different phrases which are detrimental to image recognition for time-frequency distribution. To eliminate the effects of phase differences and extract the inherent features of time-frequency distributions, a multiscale singular value manifold method is proposed. The obtained low-dimensional multiscale singular value manifold features can reveal the differences of different fault patterns and they are applicable to classification and diagnosis. Experimental verification proves that the performance of the proposed method is superior in rotating machinery fault diagnosis.     

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。     

Fault diagnosis of partial rub and looseness in rotating machinery using Hilbert-Huang transform

Partial rub and looseness are common faults in rotating machinery because of the clearance between the rotor and the stator. These problems cause malfunctions in rotating machinery and create strange vibrations coming from impact and friction. However, non-linear and non-stationary signals due to impact and friction are difficult to identify. Therefore, exact time and frequency information is needed for identifying these signals. For this purpose, a newly developed time-frequency analysis method, HHT (Hilbert-Huang Transform), is applied to the signals of partial rub and looseness from the experiment using RK-4 rotor kit. Conventional signal processing methods such as FFT, STFT and CWT were compared to verify the effectiveness of fault diagnosis using HHT. The results showed that the impact signals were generated regularly when partial rub occurred, but the intermittent impact and friction signals were generated irregularly when looseness occurred. The time and frequency information was represented exactly by using HHT in both cases, which makes clear fault diagnosis between partial rub and looseness. This paper was recommended for publication in revised form by Associate Editor Eung-Soo Shin     

基于多元经验模态分解的旋转机械早期故障诊断方法*

针对旋转机械早期微弱故障诊断问题,提出了基于多元经验模态分解的旋转机械早期故障诊断新方法。首先将多个加速度传感器合理布置在轴承座的关键位置,同步采集多通道振动信息;再利用多元经验模态分解同时对多通道振动信号进行自适应分解,得到一系列多元IMF分量;最后,依据峭度准则和相关系数从中选取包含故障主要信息的IMF分量进行信号重构,提取故障特征。多元经验模态分解方法克服了EMD等方法在进行多通道数据融合时缺乏理论依据的局限性。仿真信号和旋转机械故障信号的实验结果表明,该方法明显优于EEMD方法,对齿轮和滚动轴承故障的检测精度更高,可以在强背景噪声情况下更好地提取出故障冲击特征。