基于二叉树的支持向量机旋转机械故障诊断方法

支持向量机理论最初是针对两类模式识别问题而提出的.在故障诊断领域,多类故障诊断问题更为普遍.针对支持向量机常用的多类分类算法进行了分析,在此基础上提出了一种基于聚类思想的二叉树多类分类算法,并运用该算法对转子模拟试验台几种典型的故障进行了模式识别,实验结果表明新方法能有效地、准确地识别故障模式,比较符合实际工程要求,具有较高的推广性能.     

基于遗传算法的多尺度支持向量机及其在机械故障诊断中的应用

通过对支持向量机核函数的分析发现,当对样本的各个特征赋予不同大小的尺度参数时,可以避免冗余特征干扰分类,增强关键特征在分类中的作用,提高支持向量机分类器的学习和泛化能力。在此基础上,提出一种具有不同特征尺度参数的支持向量机(简称多尺度支持向量机),并通过遗传算法最小化LOO(leave-one-out)泛化错误上限估计,根据各个特征的识别能力赋予其不同大小的尺度参数。将多尺度支持向量机用于轴承故障诊断,实验结果表明,与传统的单尺度参数支持向量机相比,多尺度支持向量机具有更好的泛化能力。对压缩机气阀的故障识别表明,尺度参数的大小直接反映了对应特征识别能力的大小,因此可以依据尺度参数的大小进行特征选择,保留关键特征,剔除冗余特征。     

基于支持向量机的矿井提升机制动系统的故障诊断

故障样本缺乏是制约智能故障诊断发展的重要原因,支持向量机是近年来提出的一种基于小样本的统计学习方法.将支持向量机分类算法应用到提升机制动系统的多类故障分类,并与BP神经网络进行对比研究,实验表明,支持向量机算法比BP神经网络具有更好的分类性能,且 "一对多"支持向量机的分类效果是最好的,更适合于提升机制动系统的故障诊断.     

支持向量机在船舶主机诊断中的应用

为了简化船舶主机故障诊断,提高诊断效率,文章采用了支持向量机的故障诊断原理,通过小波包分解提取信号的特征参数,再将特征量送入故障分类器中进行训练,即可得出诊断结果。当数据样本较少时,采用支持向量机与采用神经网络诊断相比,具有算法简单、故障分类能力强的优点。     

基于网格支持矢量机的涡轮泵多故障诊断

支持矢量机是一种基于结构风险最小化原则的机器学习方法,对小样本决策具有较好的学习推广性.由于常规支持矢量机算法是从二类分类问题推导得出的,在解决故障诊断这种典型的多类分类问题时存在困难,为此提出一种网络支持矢量机多类分类算法,用每个类别和其他两个至四个类别构造二类支持矢量机分类器.这些二类支持矢量机分类器组合而成的网格式结构多类分类器,具有容易扩展、重复训练样本少、速度快和识别正确率高的优点.将网格式结构多类分类器应用于涡轮泵试验台多故障诊断获得了令人满意的效果.     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

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.     

基于遗传退火优化MSVM的齿轮箱故障诊断

为了实现齿轮箱典型故障的自适应准确辨识,提出一种遗传退火算法优化多核支持向量机的齿轮箱故障诊断模型。首先,将齿轮箱故障振动信号经验模式分解为多个内禀模态分量并提取其幅值能量特征;然后,再基于高斯核和多项式核构建多核支持向量机;最后,将表征齿轮箱故障特征的内禀模态分量能量输入到遗传退火算法优化的多核支持向量机进行故障模式辨识。理论分析表明,多核支持向量机能够逼近任意多元连续函数,遗传退火参数优化可快速准确得到多核支持向量机的全局最优参数向量。通过齿轮箱的故障模拟实验验证了该方法的有效性,结果表明,相比于传统的故障诊断模型,该方法显著提高了齿轮箱典型故障的诊断精度和泛化推广能力。     

一种基于EMD与多特征支持向量机(SVM)故障诊断方法

针对齿轮箱故障振动信号的不平稳非线性冲击行为,本文提出了一种基于经验模态分解的特征值提取及多特征支持向量机的智能诊断方法。在电机频率分别取30 Hz、35 Hz、40 Hz;载荷分别取0 N∙M、15 N∙M、30 N∙M;采样频率为1500 Hz条件下,进行齿轮正常状态、齿面磨损和齿轮裂痕故障模拟实验。试验结果表明:该创新方法在有限样本数据分析中可以准确、有效地对齿轮箱的工作状态和故障类型进行分类,且支持向量机在故障诊断中使用方便,可以提高诊断的精确性,在齿轮箱故障诊断或类似振动信号的检测应用中具有很强的实用性。     

基于局部s变换和极限学习机的柱塞泵滑靴磨损故障诊断

针对轴向柱塞泵结构复杂、故障信号微弱且易受噪声干扰难以进行故障模式识别的问题,提出一种基于局部s变换和极限学习机的柱塞泵故障诊断方法,对不同程度滑靴磨损故障进行诊断。采集柱塞泵在正常和不同故障状态下的振动信号进行局部s变换,对不同的特征向量组进行定性和定量比较,选择提取s矩阵最大奇异值、转轴振动基频能量占比和柱塞振动基频能量占比共三维特征向量,将特征向量输入极限学习机完成故障模式的识别,识别精度可达99%以上。最后将极限学习机与支撑向量机分类结果进行对比,证明所提故障诊断方法可以用较少的特征向量获得较高的诊断精度。     

基于支持向量机和小波分解的气体识别研究

提出将支持向量机应用到气体种类识别的研究中，并建立小波分解提取特征量和支持向量机识别气体种类的气体定性分析模型。通过小波分解提取半导体气体传感器在温度调制下的动态响应特性的特征量，分别使用不同核函数和不同结构的支持向量机建立判断特征量与气体种类的模型。实验结果说明使用支持向量机进行气体成分定性识别的效果优于同结构的神经网络，且对支持向量机自身结构的选择不敏感，适合于对多组分气体定性分析研究。建立的模型在分辨力为13ppm（对CO）和15ppm（对Hz）的条件下，对单一氢气、一氧化碳及其混合气体的识别率可达98％，适合于工程应用。     

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.     

基于柔性形态滤波和支持矢量机的滚动轴承故障诊断方法

针对滚动轴承故障振动信号的强噪声背景以及现实中不易获取大量典型故障样本的特点,提出一种基于柔性形态滤波和支持矢量机(Support vector machine, SVM)的滚动轴承故障诊断方法。柔性形态滤波既可以有效地提取出信号的边缘轮廓和信号的形状特征,同时又具有稳健性;SVM具有良好的分类性能,特别在小样本、非线性及高维特征空间中具有较好的推广能力;SVM分类器的惩罚因子和核函数参数采用经典粒子群优化算法进行优化,避免传统方法对初始点和样本的依赖。首先对振动信号进行柔性形态滤波,然后提取滤波后信号的故障特征频率的归一化能量为特征矢量作为SVM分类器的输入参数,用于区分滚动轴承的外圈、内圈和滚动体故障,SVM分类器的参数采用标准粒子群优化算法进行优化。试验结果表明了方法的有效性。     

Automatic progressive damage detection of rotor bar in induction motor using vibration analysis and multiple classifiers

There is an increased interest in developing reliable condition monitoring and fault diagnosis systems of machines like induction motors; such interest is not only in the final phase of the failure but also at early stages. In this paper, several levels of damage of rotor bars under different load conditions are identified by means of vibration signals. The importance of this work relies on a simple but effective automatic detection algorithm of the damage before a break occurs. The feature extraction is based on discrete wavelet analysis and autocorrelation process. Then, the automatic classification of the fault degree is carried out by a binary classification tree. In each node, comparing the learned levels of the breaking off correctly identifies the fault degree. The best results of classification are obtained employing computational intelligence techniques like support vector machines, multilayer perceptron, and the k-NN algorithm, with a proper selection of their optimal parameters.     

旋转机械基于信号的故障检测分析

旋转机械是各种类型机械设备中数量最多、应用最广泛的一类机械,介绍了基于信号处理的旋转机械故障诊断,并给出了支持向量机下模式识别与故障检测的方法,对压缩机进行了实验研究,证明了方法的有效性。该方法可以广泛应用到工程实际中,为有关人员起到一定的参考作用。     

基于支持向量机的多故障分类器及应用

针对因缺少大量故障数据样本而制约机械故障智能诊断的问题 ,本文改进了支持向量机多故障分类算法 ,依据此算法建立了多故障分类器 ,并应用于汽轮发电机组的故障诊断。应用结果表明 ,不必进行信号预处理以提取特征量 ,只需要用少量的时域故障数据样本建立故障分类器。该故障分类器可实现多故障的识别和诊断 ,并且具有算法简单、可对故障在线分类和故障分类能力强的优点     

Multiclass fault diagnosis in gears using support vector machine algorithms based on frequency domain data

As a dominant machine learning method, the support vector machine is known to have good generalization capability in its application of the multiclass machine–fault classification utility. In this paper, an application of the SVM in multiclass gear–fault diagnosis has been studied when the gear vibration data in frequency domain averaged over a large number of samples is used. It is established that the SVM classifier has excellent multiclass classification accuracy when the training data and testing data are at identical angular speeds. However, this method relies on the availability of both the training and testing data at that particular angular speed of the gear operation. But the training data may not always be available at all angular speeds of the gear. Hence, two novel techniques, namely the interpolation and the extrapolation methods, have been proposed; these techniques that help the SVM classifier perform multiclass gear fault diagnosis with noticeable accuracy, even in the absence of the training data at the testing angular speed. This method is based on interpolating and extrapolating the training data at angular speeds near the speeds of the test data. In this study effects of choice over different kernels and parameters of SVM on its overall classification accuracy has been studied and optimum values for these are suggested. Finally, the effect on length of training data and data density on the SVM accuracy is also presented.     

基于优化支持向量机的轴承故障诊断方法研究

以滚动轴承在正常、内圈故障、外圈故障和滚动体故障四种工况下的振动信号为研究对象,采用小波包变换的方法提取信号的能量熵,构成振动信号的特征向量。在此基础上采用支持向量机进行故障模式识别,建立支持向量机模型需要选择适当的核函数及相关参数,使用径向基核函数,需要设置的参数为核函数的宽度和误差惩罚系数,分别结合传统的网格搜索,遗传算法,粒子群算法优化支持向量机参数以提升分类性能。试验结果表明,采用优化后的支持向量机进行故障诊断可以大大提高诊断精度。     

应用支持向量机进行棉麻纤维自动识别的研究

纤维种类识别是进行纺织品混纺含量测定的前提条件,本文根据纤维直径和特定着色剂染色后颜色特征,应用支持向量机实现棉麻纤维的计算机自动识别,指出支持向量机是一种对有限训练样本更为科学的判别方法。