ELMD及核密度估计的滚动轴承故障诊断

针对滚动轴承非平稳性的振动信号,提出了基于总体局域均值分解(Ensemble Local Mean Decomposition,ELMD)及核密度估计的滚动轴承故障诊断方法。首先,对振动信号进行ELMD分解,获得一系列乘积函数(Production Function,PF),计算包含主要故障的PF分量的有效值、峭度、偏度系数,将其组合成特征向量;根据核密度估计的特性提出基于核密度估计的分类器,将特征向量输入分类器进行训练与测试,从而识别滚动轴承的工作状态和故障类型。实验结果表明,该方法能够有效的对滚动轴承故障进行识别,且效果较LMD方法好。     

基于复小波变换相位谱的齿轮故障诊断

提出了一种基于复小波变换诊断齿轮故障的新方法。利用Mexican-hat调制复小波基函数对齿轮振动信号进行连续小波变换，再作相位的频谱分析，可以突出边频带结构，识别不同故障模式。试验数据的分析结果表明，该方法适用于齿轮故障诊断，与传统的自功率谱方法以及基于实值小波的小波变换方法相比，这种方法效果更好。     

S能量分布特征和SVM在齿轮故障诊断中的应用

准确提取振动信号特征,是齿轮故障诊断的关键问题。为此,提出了一种基于S变换能量分布特征和SVM的故障诊断方法。首先对齿轮故障信号进行S变换得到时频矩阵,然后利用该矩阵构建能量分布特征。最后建立SVM齿轮故障识别模型,将对应的特征样本输入到模型中进行训练和识别,以达到对齿轮故障的准确分类。将所提出的方法应用于齿轮故障检测和诊断。通过实际故障实验数据对所提方法进行了验证。结果表明,该方法能够有效地降低噪声的影响,能够准确地识别齿轮故障,具有较高的准确率和使用价值。     

基于广义S变换与双向2DPCA的轴承故障诊断

将轴承故障诊断问题转化为故障信号时频图像的识别问题,提出一种采用双向二维主成分分析(two-directional,two-dimensional,principal component analysis,简称TD-2DPCA)的时频图像矩阵特征提取方法。首先,利用广义S变换将轴承故障信号变换为时频域图像,采用一种双向压缩的二维PCA方法对图像信息进行特征提取;然后,进行了轴承故障试验,分别采集了轴承在正常、内圈故障及外圈故障状态下的振动信号,采用所述方法对轴承3种状态下的时频分布图像进行特征提取,并根据集成矩阵距离(assembled matrix distance,简称AMD)实现图像的分类识别。试验结果表明,结合广义S变换的双向2DPCA特征提取算法可有效提高计算效率,同时具有良好的诊断性能。     

基于小波变换-模糊聚类的变速箱齿轮故障诊断

在对车辆变速箱齿轮振动加速度信号进行小波变换的基础上，提出了基于尺度-能量谱的特征提取和模糊聚类相结合的车辆变速箱齿轮故障诊断方法。该方法应用于LC5T81变速箱齿轮的故障诊断中，能够比较准确地识别与诊断出变速箱齿轮的走合运行状态、磨损运行状态和故障运行状态，具有一定的实用价值。     

基于频率切片小波变换时频分析的齿轮故障诊断

为了在强噪声中有效提取齿轮的故障特征,提出了一种基于频率切片小波变换时频分析的齿轮故障诊断方法。先对信号进行频率切片小波变换,得到在全频带下的时频分布,然后在此基础上分割出含有故障特征的时频区域,再通过对该区域进行时频阈值滤波和逆变换重构分离出有效的故障特征。仿真实验和实测信号分析表明,这种方法可从噪声信号中分离出有效的特征分量,在齿轮故障诊断方面取得了较好的应用效果。     

基于广义S变换的齿轮箱轴承故障诊断方法

齿轮箱轴承故障冲击信号通常具有非平稳和非线性的特点,若在强背景信号与噪声中,更加难以识别和提取。鉴于广义S变换具有良好的自适应性和时频聚集性,提出了一种基于广义S变换的齿轮箱滚动轴承故障诊断方法。对广义S变换的时窗函数进行讨论,分析了不同参数对调节窗函数宽度的影响;讨论了不同程度的轴承故障在广义S变换时频谱图上的能量分布;通过仿真和实验验证了所提方法的可行性。结果表明,广义S变换方法能有效地反映不同轴承故障的特征频率,为齿轮箱的轴承故障诊断提供一种有效的方法。     

基于同步压缩广义S变换的发动机故障诊断

为提高发动机故障诊断准确率,提出了基于同步压缩广义S变换（synchrosqueezing generalized S?transform,简称SSGST）与中心对称局部二值模式（center?symmetric local binary patterm,简称CSLBP）的故障诊断方法。首先,针对信号时频分析中的能量泄露、频谱涂抹、频带混叠和时频分辨率较低的问题,基于同步压缩算法与广义S变换提出了SSGST,对缸盖振动信号进行时频分析得到时频聚集性较高的二维时频图;然后,利用CSLBP提取缸盖振动信号时频图的纹理谱特征,并将其输入交叉验证寻优的核极限学习机对发动机进行故障诊断。实验结果表明,SSGST的能量聚集效果好,时频分辨率高,各频带分布较窄且不存在混叠,能够有效分离出非线性混合信号中的各频带分量;时频图的CSLBP纹理谱特征维数较低,且具有良好的类内聚集性和类间离散性;利用交叉验证寻优的KELM对故障特征进行分类,实现发动机故障诊断,获得了较高的诊断速度和精度。     

S变换用于滚动轴承故障信号冲击特征提取

为从低信噪比的滚动轴承故障信号中提取出冲击特征,以便于进行轴承故障诊断,引入S变换的信号处理方法。以短时傅里叶变换（short time Fourier transform,简称STFT）以及连续小波变换（continuous wavelet transform,简称CWT）为理论基础,分别推导得出了连续S变换的定义式,并利用快速傅里叶变换（fast Fourier transform,简称FFT）实现S变换离散化计算。S变换克服了STFT时频分辨率固定的缺点,弥补了CWT缺乏相位信息的不足。仿真信号研究表明,S变换在信号整个频带上具有良好的时频分辨率和时频聚集性,能够提取低信噪比信号中的冲击特征,且性能优于STFT和CWT。最后对一组实际的滚动球轴承故障振动信号进行S变换处理,结果表明,S变换能够方便有效地从中提取出周期性的冲击特征,从而指导滚动轴承相关故障的诊断。     

基于核密度估计的实时剩余寿命预测

现有的许多设备由于自身故障样本数据不足、少有同类故障样本数据等,寿命预测研究时往往需要进行模型结构假设及参数估计。针对这类研究方法估计不够准确的问题,提出一种基于核密度估计的非参数实时剩余寿命预测方法。该方法利用能表征部件连续退化的特征量构建退化分布的核密度估计模型,进而得到剩余寿命的概率分布函数。在实时监测不断获得新的退化特征数据后,利用已知样本的核密度估计不断递推更新得到新增样本后的核密度估计,从而进一步实现对预测剩余寿命分布的更新。通过实例分析,验证了该方法在剩余寿命预测中的有效性。     

基于估计点的双窗宽核密度估计算法

针对核密度估计中窗宽确定困难的问题,提出了基于估计点的滑动双窗宽核密度估计算法.通过采用固定窗宽的密度估计函数代替假设的正态分布密度函数以及增加求解二次导数的窗宽的方法,对估计域中的每一估计点求取其最优窗宽值,实现了窗宽根据样本的分布情况,在不同的估计点自动调整窗宽的取值.文中给出了算法的具体推导以及实现步骤,给出了多...     

基于Elman神经网络的齿轮箱故障诊断研究

1前言目前,旋转机械向着高转速、高功率、高可靠性、大型化、可测试性、不可接近性或不需接近性的趋势发展,这类系统一旦发生事故,就可能造成人员和财产的巨大损失。因此,切实保障现复杂系统的可靠性与安全性,具有十分重要的意义。发动机变速箱是整机进行减速增扭的部件,     

基于包络谱分析的滚动轴承故障诊断分析

介绍了包络谱分析方法的基本原理,它是一种基于滤波检波的振动信号处理方法,也是诊断设备零件损伤故障的一种有效的手段,尤其对初期故障和信噪比比较第的故障信号,识别能力很强。重点分析了包络谱分析方法在轴承故障诊断中的应用。通过对滚动轴承故障诊断的实例分析,验证了包络谱分析运用于诊断设备零件损伤故障所取得的效果。     

Gear fault diagnosis using active noise cancellation and adaptive wavelet transform

Acoustic signal from a gear mesh with faulty gears is in general non-stationary and noisy in nature. Present work demonstrates improvement of Signal to Noise Ratio (SNR) by using an active noise cancellation (ANC) method for removing the noise. The active noise cancellation technique is designed with the help of a Finite Impulse Response (FIR) based Least Mean Square (LMS) adaptive filter. The acoustic signal from the healthy gear mesh has been used as the reference signal in the adaptive filter. Inadequacy of the continuous wavelet transform to provide good time–frequency information to identify and localize the defect has been removed by processing the denoised signal using an adaptive wavelet technique. The adaptive wavelet is designed from the signal pattern and used as mother wavelet in the continuous wavelet transform (CWT). The CWT coefficients so generated are compared with the standard wavelet based scalograms and are shown to be apposite in analyzing the acoustic signal. A synthetic signal is simulated to conceptualize and evaluate the effectiveness of the proposed method. Synthetic signal analysis also offers vital clues about the suitability of the ANC as a denoising tool, where the error signal is the denoised signal. The experimental validation of the proposed method is presented using a customized gear drive test setup by introducing gears with seeded defects in one or more of their teeth. Measurement of the angles between two or more damaged teeth with a high level of accuracy is shown to be possible using the proposed algorithm. Experiments reveal that acoustic signal analysis can be used as a suitable contactless alternative for precise gear defect identification and gear health monitoring.     

基于核schur正交局部Fisher判别的转子故障诊断

为更好识别转子系统故障,将核映射和向量正交思想引入到局部Fisher判别分析(LFDA)中,提出基于核schur正交局部Fisher判别(KSOLFD)的流形学习算法。首先通过核函数将故障特征信号映射到高维子空间,在该空间重新定义类内散度和类间散度,构建核局部Fisher判别函数,在特征值求解时以schur正交方式找出最优投影向量,进行故障诊断。算法保证了数据降维时重构误差最小,提高了故障诊断效果。实验表明,KSOLFD故障诊断效果相对其他流形学习算法最好。     

基于Teager-Huang变换的滚动轴承故障诊断方法

针对滚动轴承故障振动信号的非平稳特征,介绍了一种基于Teager-Huang时频谱和边际谱的滚动轴承故障诊断方法。详细阐述了Teager-Huang时频谱和边际谱的计算方法及物理意义。给出了该故障诊断方法的步骤,并对仿真和实际轴承的滚动体故障、内圈故障和外圈故障信号进行了分析和故障诊断。结果表明,基于Teager-Huang变换的故障诊断方法具有计算速度快,估计准确稳定的特点,是准确判断滚动轴承故障状态的一种有效新方法。     

基于经验小波变换的机械故障诊断方法研究

经验小波变换(EWT)是一种新的自适应信号分解方法,该方法继承了 EMD 和小波分析方法的各自优点,通过提取频域极大值点自适应地分割傅里叶频谱以分离不同的模态,然后在频域自适应地构造带通滤波器组从而构造正交小波函数,以提取具有紧支撑傅立叶频谱的调幅-调频(AM-FM)成分。本文将该方法引用到机械故障诊断中,提出了一种基于经验小波变换的机械故障诊断方法,并与EMD方法进行了对比分析。仿真结果表明,经验小波变换方法明显优于EMD方法,能有效地分解出信号的固有模态。与 EMD 相比较,该方法具有分解的模态少,不存在虚假的模态,计算量小,且在理论上具有易理解性等特点。最后将该方法应用到转子碰磨故障诊断中,实验结果进一步验证了该方法的有效性,能够有效地揭示出碰磨故障数据的频率结构,区分碰磨故障的严重程度。     

基于小波分析和修正指数分布的齿轮故障诊断方法

提出了基于小波分析和修正指数分布(modifiedexponentialdistribution,MED)的齿轮故障诊断方法,该方法采用小波包将齿轮振动信号分解为若干个频率段,然后选择合适的频率段进行小波包重构,对重构后的信号进行MED分析,得到齿轮振动信号的小波包时-频分布,进而从中提取齿轮振动信号故障的故障特征.对具有裂纹的齿轮振动信号分析结果表明了基于小波分析和MED的齿轮故障诊断方法的有效性.     

Gear fault identification and classification of singular value decomposition based on Hilbert-Huang transform

An improved singular value decomposition method of gear fault identification based on Hilbert-Huang transform was proposed to overcome the problem of reconstructing a feature matrix of singular value decomposition. The method includes three steps. First, the instantaneous frequency and amplitude matrices were acquired by Hilbert-Huang transform from faulted gear signals. Second, after the matrices were decomposed by singular value decomposition, the defined distances of singular value vectors and the optimal threshold of the distance for classification were calculated. Third, the fault characteristics of a gearbox were identified and classified by the threshold of the distances. The result demonstrates that the proposed method effectively identifies the gear fault and can realize an automatic gear fault diagnosis.     

Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis

Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing.