应用小波包和包络分析的滚动轴承故障诊断

提出了一种基于小波包分析、频带能量分析和包络分析相结合的滚动轴承故障诊断方法.首先利用小波包将滚动轴承振动信号分解到不同的节点上.然后求出各频率段的能量,根据频带能量的变化情况,找出滚动轴承的故障所在的频带.最后对故障频带的重构信号做包络谱,将谱峰处的频率同滚动轴承的故障特征频率进行对比分析,诊断出滚动轴承的故障.通过对试验中采集到的滚动轴承振动信号进行分析,证明了该方法在滚动轴承故障诊断中的有效性.     

基于最小二乘支持向量机滚动轴承故障诊断

根据滚动轴承故障时振动信号特点,提出了一种基于小波包变换和最小二乘支持向量机(LS-SVM)相结合的滚动轴承故障诊断方法.通过对滚动轴承振动信号进行小波包分解,得到各分解节点对应频率段的重构信号以及各节点的能量,并将各节点能量组成的特征向量作为诊断模型的特征向量,输入到LS-SVM多类分类器中进行故障识别,然后在滚动轴承故障试验台上实测振动数据.分析结果表明,该方法具有较高的分类速度和较好的故障诊断正确率.     

基于小波包变换与神经网络的齿轮故障诊断方法

对齿轮箱故障诊断问题进行研究,由于齿轮的振动信号是非平稳信号,常规的齿轮特征提取方法难以从振动信号中提取有效故障特征信息。笔者采用小波包理论对齿轮振动信号应用db12小波进行多层分解后,从而对信号进行消噪,并对消噪后的信号进行小波包3层分解及系数重构,再次对各频段能量进行处理分析从而得到特征向量。最终应用归一化方法对特征向量处理后再结合RBF神经网络进行故障诊断,并且取得了良好的诊断效果。     

小波-神经网络在齿轮故障诊断中的应用

基于齿轮箱故障齿轮的特征提取,提出了将小波包分析与神经网络结合的齿轮故障诊断方法.对齿轮信号进行3层小波包分解,构造小波包特征向量作为故障样本.用训练好的BP神经网络进行故障诊断,实验结果表明该方法能够有效地诊断出齿轮的故障类型.     

基于小波包和AR谱分析的滚动轴承故障诊断

针对滚动轴承故障振动信号的非平稳性,提出了一种基于小波包和AR谱分析的滚动轴承故障诊断方法.该方法对系统输出信号进行小波包分解,然后进行重构,再对重构信号进行AR谱分析,从而提取出故障特征频率.试验结果表明,这种方法能有效地提取滚动轴承的故障特征,诊断其故障.     

基于小波包能量谱齿轮振动信号的分析与故障诊断

小波包是继小波分析之后提出的一种新型的多尺度分析方法,解决了小波分析在高频部分分辨率差的缺点,体现了比小波分析更好的处理效果.测试了齿轮传动系统在几种不同故障类型下的振动信号,利用小波包变换的分解和重构算法,有效地提取出齿轮故障特征信号,得到试验结果.通过比较时域分析、频域分析和小波包分析对齿轮振动信号进行的特征提取,...     

基于小波包能量谱和包络分析细化的轴承故障诊断

提出了一种将小波包能量法和细化包络分析相结合的滚动轴承故障诊断方法。首先利用小波包变换将滚动轴承振动信号分解到独立的频段上,计算出不同频率段的能量,根据频段能量的变化情况,确定滚动轴承故障所在频段。重构故障频段信号。然后应用Hilbert变换对重构信号实现包络解调,提取故障特征频率。最后为了进一步提高包络谱的分辨率,采用线性调频Z变换细化频谱。实际的滚动轴承实验数据的处理和分析结果表明,该方法在滚动轴承故障诊断中是有效的。     

基于小波分析对变速箱的故障诊断

为确定某型号液力叉车变速箱的故障原因,对其进行故障诊断,简要分析了变速箱内部结构,并计算了各档位情况下各个齿轮和轴承的故障特征频率,搭载实验平台,采集变速箱运转过程中的振动信号,并运用小波阈值去噪法对原始振动信号进行了消噪,接着分别运用小波分析和小波包分析方法对振动信号进行分解,并对相应频段信号进行重构和谱分析,综合两种分析方法的结果,获得变速箱的故障特征频率,确定是输入轴上齿轮存在缺陷。该研究对齿轮及轴承等相关构件的故障诊断有一定的参考意义。     

基于小波包能量谱和Hilbert包络分析的行星齿轮故障诊断

行星齿轮啮合振动信号噪声干扰大,难以诊断齿轮的故障。提出一种基于扭转振动信号的行星齿轮故障诊断方法。结合小波包能量谱和Hilbert包络分析用于轴系扭转振动信号进行识别行星齿轮的早期故障。将这种方法应用于行星齿轮箱在行星齿轮磨损和行星齿轮出现断齿故障状态下采集到的实际故障行星齿轮扭转振动信号,发现这种方法能识别两种状态下的行星齿轮故障特征。将小波包能量谱和Hilbert包络分析应用于两种故障状态下的横向振动信号,发现行星齿轮磨损故障不能准确识别。实验结果表明新方法对行星齿轮早期故障的识别更敏感、准确。     

小波一神经网络在齿轮故障诊断中的应用

基于齿轮箱故障齿轮的特征提取,提出了将小波包分析与神经网络结合的齿轮故障诊断方法。对齿轮信号进行3层小波包分解,构造小波包特征向量作为故障样本,用训练好的BP神经网络进行故障诊断,实验结果表明该方法能够有效地诊断出齿轮的故障类型。     

The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform

The vibration signal of a gear system is selected as the original information of fault diagnosis and the gear system vibration equipment is established. The vibration acceleration signals of the normal gear, gear with tooth root crack fault, gear with pitch crack fault, gear with tooth wear fault and gear with multi-fault (tooth root crack & tooth wear fault) is collected in four kinds of speed conditions such as 300 rpm, 900 rpm, 1200 rpm and 1500 rpm. Using the method of wavelet threshold de-noising to denoise the original signal and decomposing the denoising signal utilizing the wavelet packet transform, then 16 frequency bands of decomposed signal are got. After restructuring the decomposing signal and obtaining the signal energy in each frequency band, the signal energy of the 16 bands is as the shortlisted fault characteristic data. Based on this, using the methods of principal component analysis (short for PCA) and kernel principal component analysis (short for KPCA) to extract the feature from the fault features of shortlisted 16-dimensional data feature, then the effect of reducing dimension analysis are compared. The fault classifications are displayed through the information that got from the first and the second principal component and kernel principal component, and these demonstrate they have a different and good effect of classification. Meanwhile, the article discusses the effect of feature extraction and classification that caused by the kernel function and the different options of its parameters. These provide a new method for a gear system fault feature extraction and classification.     

Application of energies of optimal frequency bands for fault diagnosis based on modified distance function

Low-dimensional relevant feature sets are ideal to avoid extra data mining for classification. The current work investigates the feasibility of utilizing energies of vibration signals in optimal frequency bands as features for machine fault diagnosis application. Energies in different frequency bands were derived based on Parseval's theorem. The optimal feature sets were extracted by optimization of the related frequency bands using genetic algorithm and a Modified distance function (MDF). The frequency bands and the number of bands were optimized based on the MDF. The MDF is designed to a) maximize the distance between centers of classes, b) minimize the dispersion of features in each class separately, and c) minimize dimension of extracted feature sets. The experimental signals in two different gearboxes were used to demonstrate the efficiency of the presented technique. The results show the effectiveness of the presented technique in gear fault diagnosis application.     

齿轮振动信号特征的小波包频率表示法

通过对信号的小波包分解的研究．提出了信号特征的小波包频率表示方法．表示信号对分解节点和频率的功率谱分布;同时提出以小波包频率表示为依据的特征信号重建方法。齿轮振动信号特征的小波包频率表示表明该表示方法能有效展示齿轮的技术状况的变化。将功率谱集中的相邻的结点上的分解结果重构,得到的时域特征信号也能展示齿轮的技术状况。     

Separation method of rolling bearing compound fault characteristics based on Improved harmonic wavelet packet decomposition and fast ICA

Compound fault characteristics in single-channel vibration signals of rolling bearings are difficult to separate. On the basis of improved harmonic wavelet packet decomposition and fast independent component analysis (FICA), this study proposes a new method to address this problem. First, a series of mutually independent frequency bands are obtained after harmonic wavelet packet decomposition of the initial vibration signal to satisfy the requirement that the number of observed signals must be larger than the number of source signals in the FICA algorithm. Second, the optimal frequency bands are selected based on the maximum kurtosis index and used as the input matrix of the FICA algorithm to separate the compound fault characteristics further. Lastly, accurate separation and extraction of the compound fault characteristics of the rolling bearings are realized. Results show that the proposed method can effectively separate the compound fault characteristics in the single-channel vibration signals of the bearings.

     

齿轮振动信号分解及其在故障诊断中的应用

对齿轮振动信号的测试及分解进行了研究。根据信号基频,把齿轮振动信号分解为啮合振动与旋转振动,这些振动信号可用于对齿轮状态进行定量研究。基于不同形式的齿轮振动信号,介绍了几种方法来提取信号中的故障信息。利用时域平均技术及齿轮振动信号分解理论对某齿轮箱早期故障信号进行了检测。研究表明,齿轮运动信号分解能够有效检测齿轮的各类故障,高阶加速度信号对齿轮某些类型的早期故障更加敏感。     

Use of autocorrelation of wavelet coefficients for fault diagnosis

This paper presents a novel time–frequency-based feature recognition system for gear fault diagnosis using autocorrelation of continuous wavelet coefficients (CWC). Furthermore, it introduces an original mathematical approximation of gearbox vibration signals which approximates sinusoidal components of noisy vibration signals generated from gearboxes, including incipient and serious gear failures using autocorrelation of CWC. First, the drawbacks of the continuous wavelet transform (CWT) have been eliminated using autocorrelation function. Secondly, the autocorrelation of CWC is introduced as an original pattern for fault identification in machine condition monitoring. Thirdly, a sinusoidal summation function consisting of eight terms was used to approximate the periodic waveforms generated by autocorrelation of CWC for normal gearboxes (NGs) as well as occurrences of incipient and severe gear fault (e.g. slight-worn, medium-worn, and broken-tooth gears). In other words, the size of vibration signals can be reduced with minimal loss of significant frequency content by means of the sinusoidal approximation of generated autocorrelation waveforms of CWC as reported in this paper.     

小波包能量谱在滚动轴承故障诊断中的应用

提出了基于小波包能量谱的滚动轴承故障诊断方法。首先,以6406轴承为例采集不同工况的振动信号,然后对试验数据进行小波包变换,振动信号被分解到独立的频段,不同频带内的信号能量变化反映了运行状态的改变,提取各频带小波包能量谱为特征向量,最后应用基于模糊聚类的故障诊断方法。经对大量实测数据的处理和分析,能够比较准确地诊断出轴承的故障。     

Gearbox fault detection using Hilbert and wavelet packet transform

Demodulation is an important issue in gearbox fault detection. Non-stationary modulating signals increase difficulties of demodulation. Though wavelet packet transform has better time–frequency localisation, because of the existence of meshing frequencies, their harmonics, and coupling frequencies generated by modulation, fault detection results using wavelet packet transform alone are usually unsatisfactory, especially for a multi-stage gearbox which contains close or identical frequency components. This paper proposes a new fault detection method that combines Hilbert transform and wavelet packet transform. Both simulated signals and real vibration signals collected from a gearbox dynamics simulator are used to verify the proposed method. Analysed results show that the proposed method is effective to extract modulating signal and help to detect the early gear fault.     

齿轮点蚀的多通道数据融合识别方法

针对齿轮箱振动信号中混杂其他零部件振动频率的问题,提出一种基于小波包分解独立分量分析（wavelet package independent component analysis,简称WPICA）和多维经验模式分解（multivariate empirical mode decomposition,简称MEMD）的齿轮箱齿面点蚀故障信号的多通道数据融合识别方法。首先,利用一种窄带独立分量分析(sub-band decomposition independent component analysis,简称SDICA)方法—WPICA,从水泵机组多通道信号中提取齿轮箱振源,确定齿轮箱振动包含的特征频率成分;其次,借助MEMD分解多通道机组振动信号,将所获得的多维固有模式函数(intrinsic mode function,简称IMF)进行矩阵互信息运算,完成多通道数据的融合;最后,通过定义IMF故障敏感因子,确定故障敏感IMF的阶数并获得了齿轮点蚀故障的特征频率。数据分析结果证明了本研究方法的有效性。     

基于小波分析的齿轮箱振动信号消噪处理

基于小波变换具有良好时频分析的特点,介绍了小波变换用于去除噪声的原理和方法,建立齿轮箱齿轮人工点蚀故障模拟实验台,通过Matlab对故障振动信号进行小波分析,并且和没有经过信号消噪就进行小波分解进行对比,从而得出对含噪声的非平稳信号进行消噪,再进行小波分解可以较好的提取齿轮点蚀故障特征频率的结论。