基于局部均值分解的阶次跟踪分析及其在齿轮故障诊断中的应用

针对齿轮升降速过程中故障振动信号为多分量的调制信号以及故障特征频率随转速变化的特点,将局部均值分解（LMD）与阶次跟踪分析相结合,提出了一种新的齿轮故障诊断方法。首先采用阶次重采样将齿轮的时域振动信号转换为角域平稳信号,然后对角域信号进行LMD分解,得到若干个乘积函数（PF）分量,最后对各个PF分量的瞬时幅值进行频谱分析来提取齿轮的故障特征。通过对齿轮齿根裂纹故障试验振动信号的分析可知,该方法能有效地提取齿轮故障特征。     

基于LMD能量特征的滚动轴承故障诊断方法

针对滚动轴承故障振动信号的多载波多调制特性,提出一种基于局域均值分解(local mean decomposition,简称LMD)能量特征的特征向量提取方法,并与支持向量机相结合用于滚动轴承的故障诊断。首先,采用LMD方法将复杂调制振动信号分解为若干单分量信号乘积函数(production function,简称PF);然后,对反映信号主要特征的PF基于时间轴积分,得到各PF分量能量矩并构造特征向量;最后,将其输入多分类支持向量机中,用于区分滚动轴承的故障类型与故障程度。对滚动轴承内圈故障、外圈故障及滚动体故障振动信号的分析结果表明,该方法能有效提取滚动轴承各工作状态信号的故障特征,能准确识别故障类型,同时对故障程度的判断表现出较高的识别率。     

POVMD与包络阶次谱的变工况滚动轴承故障诊断

针对变转速滚动轴承故障特征提取较难的问题,提出一种基于参数优化变分模态分解(parameter optimized variational mode decomposition,简称POVMD)与包络阶次谱的变工况滚动轴承故障诊断方法。首先,采用POVMD对变转速滚动轴承振动信号进行分解,得到若干个本征模态函数之和;其次,对各个分量的时域信号进行角域重采样,将时变信号转化为平稳信号处理,再利用Hilbert变换估计重采样后的平稳信号的包络;最后,对得到的包络信号进行阶比分析,从谱图中读取故障特征信息。将POVMD方法与经验模态分解进行了对比,仿真信号分析结果表明了POVMD方法的优越性。将提出的变转速滚动轴承故障诊断方法应用于试验数据分析,分析结果表明,所提出的方法能够实现变转速滚动轴承的故障诊断,而且诊断效果优于现有方法。     

基于角域经验小波变换的滚动轴承故障诊断

针对变转速条件下滚动轴承故障特征难以提取的问题,提出了一种基于角域经验小波变换的变转速滚动轴承故障诊断方法。该方法首先利用等角度重采样将变转速下非平稳的滚动轴承故障振动信号转化为角域平稳信号,然后应用经验小波变换(Empirical mode decomposition,EWT)对角域平稳信号进行自适应分解,得到若干个经验模态分量,最后选择峭度值最大的经验模态分量进行包络谱分析,提取出滚动轴承故障的阶比特征。为提高经验小波变换的分解效率,对其频谱分割方法进行了改进。滚动轴承故障诊断实例表明,该方法能够有效地抑制噪声等干扰成分的影响,精确提取滚动轴承故障的阶比特征,为变转速条件下的滚动轴承故障诊断提供一种有效方法。     

基于MCKD和LMD的滚动轴承早期故障诊断方法

针对局部均值分解(Local mean decomposition,简称LMD)方法难以提取滚动轴承早期微弱故障的问题,提出了基于最大相关峭度解卷积(Maximum Correlated Kurtosis Deconvolution,简称MCKD)和LMD的滚动轴承早期故障诊断方法。首先采用MCKD方法对故障信号进行降噪处理,同时增强信号中的周期成分,然后进行LMD分解,将得到的PF分量与分解前信号的相关系数作为判断标准,剔除多余低频PF分量,最后,选取有效PF集进行频谱分析,提取故障特征。通过仿真数据和真实滚动轴承故障诊断实验数据表明,该方法可有效提取早期故障特征频率信息,具有一定可靠性。     

基于转速提取和优化MSB的滚动轴承故障诊断

在变转速工况下,齿轮箱滚动轴承的振动信号呈现强烈的非平稳性特征,导致无法对其进行故障诊断。针对这一问题,提出了一种基于转速提取和优化调制信号双谱(MSB)的滚动轴承故障诊断方法。首先,利用同步提取变换(SET),从原始振动信号中提取了参考轴的瞬时转速;再对原始信号进行了预白化和最小熵反褶积(MED)滤波,得到了特征增强的降噪信号,并结合提取的转速信号进行了角域重采样,建立了阶次域调制信号双谱(MSB);基于MSB分布,构造了更能体现主导调制分量与载波分量非线性耦合程度的改进载波谱;最后,根据改进载波谱对载波切片进行了择优挑选,结合MSB和双谱相干函数构造了改进调制谱,进一步消除了噪声的干扰,从而提取到了滚动轴承的显著故障特征。研究结果表明：该方法可以用于有效提取变转速齿轮箱滚动轴承的故障特征阶次,从而实现对滚动轴承进行有效的故障诊断;与传统的诊断方法相比,该方法具有明显的优势。     

LMD及马氏距离敏感阈值的滚动轴承故障诊断

针对滚动轴承非平稳性的振动信号,提出了基于局部均值分解(Local Mean Decomposition,LMD)及马氏距离敏感阈值的滚动轴承故障诊断方法。首先,对振动信号进行LMD分解,获得一系列乘积函数(Production Function,PF),有的PF分量包含的故障信息多,有的包含的少,为此采用K-L散度法提取出主要PF分量;计算主要PF分量的时域参数指标,将其组合成特征向量,根据马氏距离提出马氏距离敏感阈值来表征不同的故障状态,取多组正常信号的特征向量均值作为标准特征向量,计算未知特征向量与标准特征向量的马氏距离敏感阈值,从而对其故障状态进行识别。试验结果表明,在不同转速下,该方法能够有效的对滚动轴承故障进行识别,且效果较EMD方法好。     

LMD能量熵和SVM相结合的滚动轴承故障诊断

为实现小样本情况下对滚动轴承进行故障检测和分析,提出了基于局部均值分解(LMD)的能量熵和支持向量机(SVM)相结合的滚动轴承故障诊断方法。利用LMD信号处理方法将滚动轴承振动信号分解成有限个乘积函数(PF)分量,通过计算PF分量的能量熵进行故障特征提取,然后将提取的特征输入到SVM分类器中进行训练及测试,最终实现对滚动轴承的故障诊断。实验数据显示,在仅有少量样本条件下,LMD能量熵和SVM相结合的方法能够精确地对滚动轴承的故障类型进行识别和分类,这表明该方法对滚动轴承故障诊断的有效性。     

改进LMD和排列熵的滚动轴承故障诊断

针对滚动轴承故障振动信号的复杂特性和局部均值分解(Local Mean Decomposition,LMD)方法存在的端点效应问题,提出了基于振动信号自相似性对左右端点两侧延拓来抑制端点效应问题的改进LMD、排列熵(Permutation Entropy,PE)及优化K-均值聚类算法相结合的轴承故障诊断方法。首先通过改进LMD将非线性、非平稳的原始故障振动信号分解出一系列的乘积函数(Production Function,PF)分量,对包含主要故障信息的PF分量提取PE值作为故障特征分量,在提取特征量的基础上,最后采用优化后的K-均值聚类算法对故障类型进行识别分类。将该方法应用在滚动轴承实验数据,实验结果表明该方法可以准确、有效的实现滚动轴承的故障诊断。     

基于形态分量分析的变工况齿轮箱故障诊断研究

齿轮箱变工况运行时表现为转速和负载的变化,其振动信号是非线性的多分量信号,变工况齿轮箱故障诊断是研究难点。首先使用数字微分的阶次跟踪方法对原始振动信号按计算得到等角度重采样时刻插值,将非平稳的振动信号转化为角域平稳信号;然后使用形态分量分析(MCA)方法从角域信号中分离出冲击、简谐分量与噪声成分,提取齿轮箱非线性、多分量信号中的故障特征;再对冲击分量做角域平均突出故障特征,最后进行瞬时功率谱分析识别齿轮是否有故障。实验分析表明,使用此方法能根据瞬时功率谱分布的阶次和角度范围识别故障,适用于变工况下的故障齿轮检测。     

A fault diagnosis approach for roller bearing based on VPMCD under variable speed condition

Essentially the fault diagnosis of roller bearing is a process of pattern recognition. However, existing pattern recognition method failed to capitalize on the nature of multivariate associations between the extracted fault features. Targeting such limitation, a new pattern recognition method – variable predictive model based class discriminate (VPMCD) is introduced into roller bearing fault identification. The VPMCD consider that all or part of the feature values will exhibit interactions in nature and these associations will have different performances between different classes, which is always true in practice when faults occur in roller bearings. Target to the characteristics of non-stationary and amplitude-modulated and frequency-modulated (AM–FM) of vibration signal picked up under variable speed condition, a fault diagnosis method based upon the VPMCD, order tracking technique and local mean decomposition (LMD) is put forward and applied to the roller bearing fault identification. Firstly, LMD and order tracking analysis method are combined to extract the fault features of roller bearing vibration signals under variable speed condition; Secondly, the feature values are regard as the input of VPMCD classifier; finally, the working condition and fault patterns of the roller bearings are identified automatically by the output of VPMCD classifier. The analysis results from experimental signals with normal and defective roller bearings indicate that the proposed fault diagnosis approach can distinguish the roller bearing status-with or without fault and fault patterns under variable speed condition accurately and effectively.     

Bearing fault detection and diagnosis based on order tracking and Teager-Huang transform

The vibration signal of the run-up or run-down process is more complex than that of the stationary process. A novel approach to fault diagnosis of roller bearing under run-up condition based on order tracking and Teager-Huang transform (THT) is presented. This method is based on order tracking, empirical mode decomposition (EMD) and Teager Kaiser energy operator (TKEO) technique. The nonstationary vibration signals are transformed from the time domain transient signal to angle domain stationary one using order tracking. EMD can adaptively decompose the vibration signal into a series of zero mean amplitude modulation-frequency modulation (AM-FM) intrinsic mode functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the AM-FM component at any instant. Experimental examples are conducted to evaluate the effectiveness of the proposed approach. The experimental results provide strong evidence that the performance of the Teager-Huang transform approach is better to that of the Hilbert-Huang transform approach for bearing fault detection and diagnosis. The Teager-Huang transform has better resolution than that of Hilbert-Huang transform. Teager-Huang transform can effectively diagnose the faults of the bearing, thus providing a viable processing tool for gearbox defect monitoring.     

A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery

In order to extract fault features of large-scale power equipment from strong background noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal–noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the vibration signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method.     

A vibration analysis method based on hybrid techniques and its application to rotating machinery

Vibration-based condition monitoring and fault diagnosis technique is a most effective approach to maintain the safe and reliable operation of rotating machinery. Unfortunately, the vibration signal always exhibits non-linear and non-stationary characteristics, which makes vibration signal analysis and fault feature extraction very difficult. To extract the significant fault features, a vibration analysis method based on hybrid techniques is proposed in this paper. Firstly, the raw signals are decomposed into a few product functions (PFs) using local mean decomposition (LMD), and meanwhile instantaneous frequency and instantaneous amplitude also are obtained. Subsequently, Fourier transform is performed on the derived PFs, and then, according to the spectra features, the useful PFs are selected to reconstruct the purified vibration signals. Lastly, several different fault features are fused to illustrate the operating state of the machinery. The experimental results show that the proposed method can accurately extract machine fault features, which proves that the combined application of LMD and other signal processing techniques is a successful scheme for the machine vibration analysis.     

基于LMD的包络谱特征值在齿轮箱故障诊断中的应用研究

针对齿轮箱故障振动信号大多是多分量的调幅-调频信号,而传统包络分析法又太依赖经验值选取参数的问题,对齿轮箱振动信号的分解方法、包络分析方法以及提取特征值等方面进行了研究,提出了一种基于局部均值分解(local mean de-composition,LMD)的包络谱特征值的方法。该方法首先利用局部均值分解对齿轮箱信号进行了处理,获得了包含有不同频率特征的PF(product function)分量,最后对包含有主要故障信息的第一级PF分量进行了包络分析,提取了包络谱的特征频率,以此来判别齿轮箱的工作状态和故障类型。利用齿轮箱正常状态、局部损伤、磨损故障3种齿轮箱振动信号的实例进行了验证。研究结果表明,利用LMD分解后求取包络谱特征频率的方法能够较为准确地判别齿轮箱的工作状态和故障类型。     

基于LMD-CM-PCA的滚动轴承故障诊断方法

为提高在非平稳工况下对滚动轴承故障的直观辨识能力,笔者提出基于LMD-CM-PCA的故障诊断方法。首先,对滚动轴承振动信号进行局部均值分解(local mean decomposition,简称LMD),提取乘积函数(product function,简称PF)矩阵;然后,计算PF矩阵与原振动信号的皮氏相关系数(pearson product-moment correlation coefficient,简称PPCC),将PFs对应的PPCC代入相关熵模型得到PF的相关熵矩阵(correntropy matrix,简称CM),CM经主成分分析(principal component analysis,简称PCA)进行特征变换得到融合相关熵矩阵(integrated correntropy matrix,简称ICM)。分别在轻微和严重故障时,对滚动轴承不同工况下的振动样本进行交叉混合,并计算其ICM。结果证明,ICM在可视维度比传统特征(如:能量矩和谱峭度)的融合特征更能隔离工况对故障可分性的干扰。LMD-CM-PCA方法为滚动轴承故障的直观辨识提供了技术支持,在故障诊断方面具有良好的应用前景。     

A Roller Bearing Fault Diagnosis Method Based on Improved LMD and SVM

Aiming at the non-stationary features of the roller bearing fault vibration signal,a roller bearing fault diagnosis method based on improved Local Mean Decomposition(LMD)and Support Vector Machine(SVM)is proposed.In this paper,firstly,the wavelet analysis is introduced to the signal decomposition and reconstruction;secondly,the LMD method is used to decompose the reconstruction signal obtained by the wavelet analysis into a number of Product Functions(PFs)that include main fault characteristics,thus,the initial feature vector matrixes could be formed automatically;Thirdly,by applying the Singular Value Decomposition(SVD)techniques to the initial feature vector matrixes,the singular values of the matrixes can be obtained,which can be used as the fault feature vectors of the roller bearing and serve as the input vectors of the SVM classifier;Finally,the recognition results can be obtained from the SVM output.The results of analysis show that the proposed method can be applied to roller bearing fault diagnosis effectively.     

基于余弦调频小波变换的滚动轴承故障研究

针对滚动轴承故障振动信号的特点，构造余玄调频小波，采用连续小波变换的方法来提取滚动轴承故障振动信号的特征，在此基础上提出了一种滚动轴承故障诊断方法：时间一小波能量谱自相关分析法。通过对滚动轴承具有缺陷的情况下振动信号的分析，说明时间一小波能量谱自相关分析法不仅能检测到滚动轴承故障的存在，而且能有效地识别滚动轴承的故障模式。     

Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

Roller bearing failure is one of the most common faults in rotating machines.Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed.But feature extraction from fault signals requires expert prior information and human labour.Recently,deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input data.Given its robust per-formance in image recognition,the convolutional neural network(CNN)architecture has been widely used to learn automatically discriminative features from vibration images and classify health conditions.This paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis.The first stage in the proposed method is to generate the RGB vibration images(RGBVIs)from the input vibration signals.To begin this process,first,the 1-D vibration signals were converted to 2-D grayscale vibration Images.Once the conversion was completed,the regions of interest(ROI)were found in the converted 2-D grayscale vibration images.Finally,to produce vibration images with more discriminative characteristics,an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images(RGBVIs)with sets of colours and texture features.In the second stage,with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health conditions.Two cases of fault classification of rolling element bearings are used to validate the proposed method.Experimental results of this investigation demonstrate that RGBVI-CNN can generate advan-tageous health condition features from bearing vibration signals and classify the health conditions under different working loads with high accuracy.Moreover,several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accuracy,precision,recall,and F-score.