Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization

A novel intelligent fault diagnosis model based on multi-kernel support vector machine (MSVM) with chaotic particle swarm optimization (CPSO) for roller bearing fault diagnosis is proposed. Multi-kernel support vector machine is a powerful new tool for roller bearing fault diagnosis with small sampling, nonlinearity and high dimension. Chaotic particle swarm optimization is developed in this study to determine the optimal parameters for MSVM with high accuracy and great generalization ability. Moreover, the feature vectors for fault diagnosis are obtained from vibration signal that preprocessed by time-domain, frequency-domain and empirical mode decomposition (EMD) and the typical manifold learning method LTSA is used to select salient features. The experimental results indicate that this proposed approach is an effective method for roller bearing fault diagnosis, which has more strong generalization ability and can achieve higher diagnostic accuracy than that of the single kernel SVM or the MSVM which parameters are randomly extracted.     

Fault diagnosis method of rolling bearing using principal component analysis and support vector machine

To effectively extract the fault feature information of rolling bearings and improve the performance of fault diagnosis, a fault diagnosis method based on principal component analysis and support vector machine was presented, and the rolling bearings signals with different fault states were collected. To address the limitation on effectively dealing with the raw vibration signals by the traditional signal processing technology based on Fourier transform, wavelet packet decomposition was employed to extract the features of bearing faults such as outer ring flaking, inner ring flaking, roller flaking and normal condition. Compared with the previous literature on fault diagnosis using principal component analysis (PCA) and support vector machine (SVM), one-to-one and one-to-many algorithms were taken into account. Additionally, the effect of four kernel functions, such as liner kernel function, polynomial kernel function, radial basis function and hyperbolic tangent kernel function, on the performance of SVM classifier was investigated, and the optimal hype-parameters of SVM classifier model were determined by genetic algorithm optimization. PCA was employed for dimension reduction, so as to reduce the computational complexity. The principal components that reached more than 95 % cumulative contribution rate were extracted by PCA and were input into SVM and BP neural network classifiers for identification. Results show that the fault feature dimensionality of the rolling bearing is reduced from 8-dimensions to 5-dimensions, which can still characterize the bearing status effectively, and the computational complexity is reduced as well. Compared with the raw feature set, PCA has a higher fault diagnosis accuracy (more than 97 %), and a shorter diagnosis time relatively. To better verify the superiority of the proposed method, SVM classification results were compared with the results of BP neural network. It is concluded that SVM classifier achieved a better performance than BP neural network classifier in terms of the classification accuracy and time-cost.     

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SPWVD时频图纹理特征的滚动轴承故障诊断

针对如何提高滚动轴承故障诊断准确率的问题,提出一种基于平滑伪维格纳-威利分布(smooth and pseudo Wigner-Ville distribution,简称SPWVD)时频图纹理特征的故障诊断方法,对滚动轴承不同故障类型及故障程度进行识别。首先,采用SPWVD时频分析方法处理轴承故障振动信号,并获取时频图,从中提取选择表征能力优秀的特征参量作为故障特征;其次,将故障特征作为输入,结合支持向量机(support vectors machine,简称SVM)建立滚动轴承故障诊断模型;最后,采用轴承故障数据,比较SPWVD时频图纹理特征、维格纳-威利分布(Wigner-Ville distribution,简称WVD)时频图纹理特征和小波尺度谱图纹理特征3种故障特征的模式识别能力及准确率。分析结果表明,SPWVD时频图纹理故障特征分类效果最佳,敏感性最强,具有较高的故障诊断精度。     

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.     

A roller bearing fault diagnosis method based on the improved ITD and RRVPMCD

Targeting that the measured vibration signal of roller bearing contains the characteristics of non-stationary and nonlinear, and the extraction features may contain smaller correlation and redundancy characteristics in the roller bearing fault diagnosis, the vibration signal processing method based upon improved ITD (intrinsic time-scale decomposition) and feature selection method based on Wrapper mode are put forward. In addition, in the design of the classifier, targeting the limitation of existing pattern recognition method, a new pattern recognition method-variable predictive model based class discriminate (VPMCD) is introduced into roller bearing fault identification. However, the parameters are fitted by using least squares in VPMCD method, while least squares regression is sensitive to “abnormal value”. Therefore, a robust regression-variable predictive mode-based class discriminate (RRVPMCD) method is proposed in this paper, robust regression is adopted to estimate parameters and the effect of “abnormal value” in the estimation of parameters would be reduced by giving each feature a weight. Firstly, improved ITD method and feature selection method based on Wrapper mode are combined to extract the fault features of roller bearing vibration signals, and feature vector matrixes are established, then a predictive model is built through the method of RRVPMCD, finally, the established predictive model is used for pattern recognition. Experimental results show that the model based on the improved ITD, the Wrapper feature selection and RRVPMCD method can effectively identify work status and fault type of roller bearing.     

基于等距特征映射和支持矢量机的转子故障诊断方法

针对振动信号的非线性特征,提出一种基于等距特征映射(Isometric feature mapping,ISOMAP)和支持矢量机(Support vector machine,SVM)的转子故障诊断方法。利用ISOMAP把数据从高维空间投影到低维空间而不改变数据内在属性的特点,对高维的故障振动信号降维并提取出低维的数据作为特征矢量,采用一种新核函数支持矢量机作为分类器进行故障诊断。将该方法应用于转子故障诊断,结果表明,ISOMAP-SVM方法不仅具有较高的故障诊断率,而且取得振动信号在低维空间的可视化表示。与其他核函数相比新核函数支持矢量机具有较好的诊断效果。     

Feature selection for machine fault diagnosis using clustering of non-negation matrix factorization

Feature selection has been attracting more attentions in recent years for its advantages in improving the fault diagnosis efficiency and reducing the cost of feature acquisition. In this paper, we regard the feature selection as a clustering process with data decomposition technique and propose a novel feature selection method based on the non-negation matrix factorization (NMF). Alternating Least Squares (ALS) algorithm with sparsity control and decorrelation constrains is adopted to factorize original feature space into two low-rank matrixes (projection vectors and feature spaces). Considering the clustering distribution of the projection space, the optimal feature vectors are calculated by the means of the best updating rule parameters. Besides, the inverse of feature vectors is furtherly utilized in the seeking feature subset, which ensures high classifying performance. Experiments are performed by using two standard data sets and the fault diagnosis of roller bearing case. The results are compared with those obtained by applying the whole feature set and standard feature selection algorithms. The outcomes of comparative analysis have confirmed the effectiveness of the proposed approach.     

基于KPCA和WPHM的滚动轴承可靠性评估与寿命预测

为了评估滚动轴承的可靠性和预测剩余使用寿命,选取能够反映性能退化过程的特征参数作为寿命预测模型的输入参数,提出一种基于核主元分析(kernel principal component analysis,简称KPCA)和威布尔比例故障率模型(Weibull proportional hazards model,简称WPHM)的方法。首先,提取滚动轴承全寿命周期的时域、频域及时频域等多特征参数,从中筛选出有效的特征参数,构建高维相对特征集;其次,进行核主元分析,选取能够反映轴承全寿命周期性能退化过程的核主元,进而作为WPHM的协变量来进行可靠性评估和剩余寿命预测。通过滚动轴承全寿命试验,验证了该方法能够对轴承进行准确的可靠性评估和剩余寿命预测,以提供及时的维修决策。同时,由于提取的是相对特征,降低了同种轴承间在制造、安装及工况的差异,增强了该方法的适用性和稳定性。     

基于经验模态分解的滚动轴承故障诊断方法

提出了一种基于经验模态分解的滚动轴承故障诊断方法，并定义了能量熵的概念。从不同状态的滚动轴承振动信号的能量熵值中发现，当滚动轴承发生故障时，各频带的能量会发生变化。为了进一步对滚动轴承的状态和故障类型进行分类，再从若干个包含主要故障信息的IMF分量中提取能量特征参数作为神经网络的输入参数来识别滚动轴承的故障类型。对滚动轴承的正常状态、内圈故障和外圈故障振动信号的分析结果表明，以经验模态分解为预处理器提取各频带能量作为特征参数的神经网络诊断方法比以小波包分析为预处理器的神经网络诊断方法有更高的故障识别率，可以准确、有效地识别滚动轴承的工作状态和故障类别。