基于局域均值分解包络谱和SVM的滚动轴承故障诊断方法研究

论述了局域均值分解(Local mean decomposition,LMD)的定义和算法。结合局域均值分解、包络分析和支持向量机(Support vector machine,SVM)的各自特点,提出了一种基于LMD包络谱和SVM的滚动轴承故障诊断方法,该方法先对滚动轴承振动信号进行分解,得到一系列的生产函数分量,然后,再对前面几个生产函数分量进行包络分析,从包络谱中提取特征幅值比作为特征向量输入到SVM分类器中进行识别。实验结果验证了提出的方法的有效性,可以有效地识别滚动轴承的不同故障。     

Fault diagnosis of rotating machine by thermography method on support vector machine

Feature-based classification techniques consist of data acquisition, preprocessing, feature representation, feature calculation, feature selection, and classifiers. They are useful for online, real-time condition monitoring and fault diagnosis / features, which are now available with the development of information technologies and various measurement techniques. In this paper, an intelligent feature-based fault diagnosis is suggested, developed, and compared with vibration signals and thermal images. Fault diagnosis is performed using thermal imaging along with support vector machine (SVM) classification to simulate machinery faults, resulting in an accuracy level comparable to vibration signals. The observed results show that fault diagnosis using thermal images for rotating machines can be applied to industrial areas as a novel intelligent fault diagnostic method with plausible accuracy. It can be also proposed as a unique non-contact method to analyze rotating systems in mass production lines within a short time.     

基于EMD和免疫参数自适应SVM的滚动轴承故障诊断

为实现滚动轴承故障的智能诊断,提出一种基于经验模式分解和免疫参数自适应支持向量机相结合的滚动轴承故障诊断方法.使用经验模式分解将故障信号分解为若干个本征模函数之和,然后通过定义的故障特征频率筛选函数,自动地从各本征模函数的包络谱中提取出包含轴承外圈、内圈及滚动体故障的特征向量.在特征提取的基础上,将改进的免疫克隆选择算法和K折交叉验证方法相结合,实现了支持向量机参数的自适应优化选取,并进一步训练得到免疫参数自适应支持向量机分类器.通过SKF6203滚动轴承数据实验表明,该方法能获得较高的故障诊断识别率.     

基于可测性分析和支持向量机的模拟电路故障诊断

针对低可测性模拟电路的测试问题,提出了一种模拟电路故障诊断的新方法.该方法首先计算被测电路的可测性,利用可测性提供的信息对被测电路进行模糊组划分,组成可诊断的元件集,并引入支持向量机完成对故障的分类识别.可测性分析理论上确定了被测电路可诊断的元件集,支持向量机结构简单,泛化能力强,以模拟和混合信号测试标准电路的实验结果证明了基于可测性分析和支持向量机的模拟电路故障诊断方法是有效的,其故障诊断率大于99%.     

Fault diagnosis of rolling element bearing using a new optimal scale morphology analysis method

Periodic transient impulses are key indicators of rolling element bearing defects. Efficient acquisition of impact impulses concerned with the defects is of much concern to the precise detection of bearing defects. However, transient features of rolling element bearing are generally immersed in stochastic noise and harmonic interference. Therefore, in this paper, a new optimal scale morphology analysis method, named adaptive multiscale combination morphological filter-hat transform (AMCMFH), is proposed for rolling element bearing fault diagnosis, which can both reduce stochastic noise and reserve signal details. In this method, firstly, an adaptive selection strategy based on the feature energy factor (FEF) is introduced to determine the optimal structuring element (SE) scale of multiscale combination morphological filter-hat transform (MCMFH). Subsequently, MCMFH containing the optimal SE scale is applied to obtain the impulse components from the bearing vibration signal. Finally, fault types of bearing are confirmed by extracting the defective frequency from envelope spectrum of the impulse components. The validity of the proposed method is verified through the simulated analysis and bearing vibration data derived from the laboratory bench. Results indicate that the proposed method has a good capability to recognize localized faults appeared on rolling element bearing from vibration signal. The study supplies a novel technique for the detection of faulty bearing.     

A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree

A new bearing vibration feature extraction method based on multiscale permutation entropy (MPE) and improved support vector machine based binary tree (ISVM-BT) is put forward in this paper. Local mean decomposition (LMD), a new self-adaptive time–frequency analysis method, is utilized to decompose the roller bearing vibration signal into a set of product functions (PFs) and then MPE method is used to characterize the complexity of the principal PF component in different scales. After the feature extraction, a new pattern recognition approach called ISVM-BT is introduced to accomplish the fault identification automatically, which has the priority of high recognition accuracy compared with other classifiers. Besides, the Laplacian score (LS) is introduced to refine the fault feature by sorting the scale factors. Finally, the rolling bearing fault diagnosis method based on LMD, MPE, LS and ISVM-BT is proposed and the experimental results indicate the proposed method is effective in identifying the different categories of rolling bearings.     

基于综合型模糊支持向量机的故障诊断方法及应用

设备信息和故障的不确定性、模糊性及故障样本的缺乏给故障诊断带来了较大的困难.针对该问题,分析了现有模糊支持向量机的原理和优缺点,提出了一种综合型模糊支持向量机.该模糊支持向量机既可以处理样本含有模糊信息的情况,又可以解决支持向量机分类中存在的不可分问题.然后,提出了基于综合型模糊支持向量机的故障诊断方法,并在某电路系统故障诊断中开展了应用研究.应用结果表明,该诊断方法在设备状态存在模糊性和故障样本较少的情况下,与现有模糊支持向量机诊断方法相比,实现了较准确的故障诊断.     

基于小波分析和支持向量机的旋转机械故障诊断方法

提出了一种基于小波分析和支持向量机相结合的旋转机械故障诊断方法.首先运用小波包对振动信号进行分解和重构,然后提取各个频带里的信号能量值,将该能量值作为特征参数输入到支持向量机,进行故障模式识别.通过对实验数据的分析表明,与BP神经网络相比,该方法可以获得更高的旋转机械故障诊断准确率.     

Fault diagnosis based on Walsh transform and support vector machine

Recognition of shaft orbit plays an important role in the fault diagnosis. In this paper, a novel recognition method for the shaft orbit based on Walsh transform and support vector machine is proposed. In the method, distance vector between the point on the shaft orbit and its center is first calculated. Then, the distance vector is transformed by Walsh matrix, and the Walsh spectrum obtained has property of invariance to rotation, scaling and translation. In the end, the Walsh spectrum, viewed as the feature of shaft orbit, is trained and tested by means of support vector machine. In addition, a comparison with the previous methods is performed, and experimental results are encouraging, which fully demonstrates the effectiveness and superiority of the proposed approach.     

多分类边界支持矩阵机及其在滚动轴承故障诊断中的应用

由于支持矩阵机(SMM)利用平行超平面实现对不同类别样本的分类,使其无法最大化任意两类样本之间间隔,为此,通过分析非平行超平面与支持矩阵机的相关理论,提出了一种多分类边界支持矩阵机(MBSMM),并将其应用于滚动轴承的故障诊断中.首先,在MBSMM中以矩阵为建模元素,建立了其多分类目标函数,充分利用输入矩阵行与列之间的...     

基于分形理论与支持向量机的回转窑故障诊断

利用支持向量机进行故障分类,对数据样本的要求较高,在数据样本某些属性的特征区分不明显的条件下,分类效果不理想。文章尝试将分形理论引入到数据样本的处理中,利用得到的分形维数替代原数据样本中的特征性不强的属性,再利用支持向量机进行分类。针对新型干法水泥生产过程中常见的6种工艺故障,运用这两种分类策略进行分类,分类准确率分别为88.6667%和99.9996%。所以运用分形理论与支持向量机相结合的方法,可以更加有效地实现水泥回转窑故障的分类诊断。     

Fault diagnosis method of rolling bearing based on deep belief network

A method based on the theory of deep learning and feature extraction and a fault diagnosis model of a rolling bearing based on deep belief network are proposed in this study considering the complex, nonlinear, and non-stationary vibration signal of the rolling bearing. To some extent, the method avoids the complex structure of deep neural network and can be easily trained. Experimental results show that the recognition rate of the method reaches 100 %. The method can identify various types of faults accurately and has good fault diagnosis capability, which can provide the convenience for maintenance.     

基于能量熵和EEMD结合的滚动轴承故障诊断方法

采用了一种基于小波包能量熵结合集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)方式完成对高速列车轴箱轴承仿真振动信号和实测振动信号的故障诊断.在完成仿真轴承应力状态分布分析和仿真振动信号故障提取方式检验以及实测振动信号时域参数分析和对轴承故障初步诊断的基础上,对振...     

Fault diagnosis using binary tree and sphere-structured support vector machines

A new method (BSSVM) using binary tree and sphere-structured support vector machines (SSVM) is presented for fault diagnosis. It constructs the hyper-planes step by step according to binary tree, partitions a class in every step and eliminates blind areas which are not insuperable for other multi-class methods of SVM. 4 common faults are created by a test-bed of rotor, their vibration signals are collected and transformed to frequency domain by FFT, then the spectrum energy in 8 bands divided by their total energy are taken as the energy distributions. With PCA, the 8-dimensional energy distributions are converted to 2-dimensional fault samples which can hold more than 80% useful information of the primary data. With the fault samples, the new method is tested and compared with several other multiclass methods of SVM, and experimental results show that the new method has higher speed and better accuracy than other similar ones.     

基于RLMD和WSET的变转速下滚动轴承故障诊断

在变转速工况下,对起重机、风机等重载设备中滚动轴承的故障诊断不够准确。针对这一问题,提出了一种基于鲁棒局部均值分解(RLMD)和同步提取小波变换(WSET)的故障诊断方法。首先,以振动信号分析为基础,研究了变转速下的滚动轴承的故障特征频率,对RLMD和WSET公式进行了推导;利用RLMD对滚动轴承振动信号进行了模式分解,以最大互相关系数原则选取了最佳分量;然后,对选取的分量进行了WSET,获得了能量集中的时频表示,从时频平面中提取了故障特征曲线,并将其与理论故障特征频率曲线进行了比较,从而进行了变转速下的故障诊断;最后,研究设计了多分量信号的数值模拟仿真实验,进行了轴承-齿轮故障综合试验台变转速滚动轴承的故障诊断。研究结果表明：所提出的方法能够很好地消除噪声的影响,获得准确的故障特征频率曲线,在变转速滚动轴承故障诊断中具有有效性。     

Fault diagnosis on production systems with support vector machine and decision trees algorithms

In this study, the operation of the didactic modular production system of the Festo Company was monitored by using eight sensors. The output of the linear potentiometer, magazine optic sensor, vacuum analog pressure sensor, material holding P/E switch, material handling arm pressure sensor, vacuum information P/E switch, optic sensor, and pressure sensor of main system were recorded while the system was operating in the perfect condition and various problems were artificially created. Some of these defects were empty magazine, zero vacuum, inappropriate material, no pressure, closed manual pressure valve, missing drilling stroke, poorly located material, not vacuuming the material and low air pressure. In all cases, one or more sensors clearly indicated the defect. The results indicated that the system support vector machine (SVM) and decision tree algorithm correctly identified all the presented cases.     

局部切空间排列和支持向量机的故障诊断模型

提出了一种非线性流形学习和支持向量机的故障诊断模型.基于机电系统振动信号时域与频域的20个特征参数构建高维特征空间,利用局部切空间排列的非线性流形学习算法提取出隐藏其中的低维流形,网格搜索算法进行维数和邻域点参数的优化,实现高维相空间中局部邻域参数的自适应选取,获得机电系统的故障特征.利用K折交叉验证和一对一法构造支持向量机多类故障分类器,采用径向基核函数支持向量机进行机电系统的故障诊断.应用于转子试验台的3种故障状态的识别并与其他故障诊断方法进行分析比较,结果表明基于局部切空间排列和支持向量机的机电系统故障诊断模型诊断精度可达到96.6667％,可以有效提取故障的敏感特征并解决机电系统故障样本缺乏的问题.     

基于WTPCA-MSVMs过程监控方法

提出了基于小波变换主元分析和多支持向量机(wavelet transform PCA-Multiple support vector machines,WTPCA-MSVMs)的过程监控方法,该方法首先利用小波变换(wavelet transform,WT)对采样数据进行预处理,以有效抑制过程数据中所含的噪声和干扰信号;然后利用主元分析(principal component analysis,PCA)对预处理后的数据建立主元监控模型;考虑到实际工业过程故障数据的数量较少,而支持向量机(support vector Machine,SVM)在小样本学习方面具有良好的泛化能力的特性,最后提出了基于多支持向量机(multiple support vector machines,MSVMs)的故障诊断方法。对TE(tennessee eastman,TE)过程的监控应用表明了所提出方法的有效性。     

A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm

Targeting the non-linear dynamic characteristics of roller bearing faulty signals, a fault feature extraction method based on hierarchical entropy (HE) is proposed in this paper. SampEns of 8 hierarchical decomposition nodes (e.g. HE at scale 4) are calculated to serve as fault feature vectors, which takes into account not only the low frequency components but also high frequency components of the bearing vibration signals. HE can extract more faulty information than multi-scale entropy (MSE) which considers only the low frequency components. After extracting HE as feature vectors, a multi-class support vector machine (SVM) is trained to achieve a prediction model by using particle swarm optimization (PSO) to seek the optimal parameters of SVM, and then ten different bearing conditions are identified through the obtained SVM model. The experimental results indicate that HE can depict the characteristics of the bearing vibration signal more accurately and more completely than MSE, and the proposed approach based on HE can identify various bearing conditions effectively and accurately and is superior to that based on MSE.     

基于非线性主成分分析的车身制造偏差源诊断

对主成分分析在轿车车身制造偏差源诊断方面的不足进行了分析,进而在其基础上提出采用非线性主成分分析,从而更有效、更准确地来对轿车车身制造偏差源进行诊断,并给出了计算实例。实例证明该方法优于主成分分析,并在工程上有较大的可用性。