采用GWO优化SVM的燃气轮机气路故障诊断

将灰狼优化算法和支持向量机算法作为理论指导,并采用灰狼优化算法对支持向量机算法进行优化,以实现燃气轮机故障类型的分类。将灰狼优化算法与遗传算法优化支持向量机方法和粒子群算法优化支持向量机方法进行对比,结果表明,通过灰狼算法优化支持向量机的方法对燃气轮机故障分类的准确率要高于遗传算法优化支持向量机算法和粒子群算法优化支持向量机的故障分类方法。     

改进自适应遗传SVM滚动轴承故障诊断

采用自适应遗传算法优化SVM相关参数,建立了IAGA-SVM滚动轴承故障诊断模型。通过实验验证了该模型的精确性和有效性。     

基于EMD和SVM的传感器故障诊断方法

为了解决自确认压力传感器的故障诊断问题,提出了一种基于经验模式分解(EMD)和支持向量机(SVM)的传感器故障诊断方法,该方法对传感器输出信号进行经验模态分解,将其分解为若干个固有模态函数(IMF),对每个IMF通过一定的削减算法增强故障特征,然后计算每个IMF和残余项的能量以及整个信号的削减比作为特征向量,以此作为输入来建立支持向量多分类机,判断传感器的故障类型.通过压力传感器的故障诊断结果表明,该方法能有效的应用于传感器的故障诊断中.     

基于EMD和SVM的数控机床主轴故障诊断

针对数控机床的主轴故障,将经验模态分解（EMD）方法和支持向量机（SVM）相结合,用于故障诊断。采用EMD将信号分解成具有不同特征尺度的本征式分量IMF,分析各IMF,通过求取均方根值提取各特征向量,然后将各特征向量输入支持向量机,建立故障分类器进行状态识别。实验结果表明,预测结果完全正确,该方法有效。     

基于二叉树和SVM的指纹分类

为解决支持向量机（Support Vector Machine，SVM）进行指纹多类分类存在困难的问题，在应用二叉树理论的基础上，提出了一种新型的指纹分类方法．该算法首先使用二叉树进行多类决策，将原始分类数据分解成3个二类分类问题，然后利用SVM进行二类分类，使3个分类超平面得到优化．两者的有机结合，充分发挥了SVM在二类分类问题方面相对于其它方法的优势，从而使算法的推广能力有较大提高，总的分类正确率可达97．9％．实验结果证明，二叉树构造多类框架将指纹多类分类问题分解成3个二类分类器系统，不仅可以有效的提高指纹分类的效率，还充分发挥了SVM分类器解决二类分类问题的优势．     

SVM模式识别技术及在机械故障诊断中的应用进展

支持向量机(Support Vector Machines,SVM)是一种基于统计学习理论的新型机器学习方法,对小样本决策具有较好的学习推广性.为在机械故障诊断中更好地运用该方法,从基于支持向量机理论的模式识别技术和机械故障诊断中应用两方面,综述了近年来支持向量机国内外研究应用现状,分析了技术特点、存在问题、解决方案及其在机械工程领域应用前景.     

基于RS和SVM的汽轮机组故障诊断方法

为了提高汽轮机组故障诊断的效率,设计并实现了基于粗糙集和多类支持向量机的融合算法。把粗糙集作为数据的前处理器,对条件属性进行知识约简和去除冗余属性以达到降低数据维数的目的。然后构造多类支持向量机分类器并用约简后的新样本数据训练。测试结果表明,基于粗糙集和支持向量机融合算法的故障诊断方法诊断速度快,推广能力强。     

基于二叉树和SVM的指纹分类

为解决支持向量机(Support Vector Machine, SVM)进行指纹多类分类存在困难的问题,在应用二叉树理论的基础上,提出了一种新型的指纹分类方法．该算法首先使用二叉树进行多类决策,将原始分类数据分解成3个二类分类问题,然后利用SVM进行二类分类,使3个分类超平面得到优化．两者的有机结合,充分发挥了SVM在二类分类问题方面相对于其它方法的优势,从而使算法的推广能力有较大提高,总的分类正确率可达97.9%．实验结果证明,二叉树构造多类框架将指纹多类分类问题分解成3个二类分类器系统,不仅可以有效的提高指纹分类的效率,还充分发挥了SVM分类器解决二类分类问题的优势．     

基于神经网络和支持向量机的故障诊断

介绍了神经网络和支持向量机理论,对比分析两种理论的优缺点.通过试验,说明了神经网络和支持向量机在故障诊断中的应用方法,并通过改变学习样本数量,比较两种智能技术在故障诊断中的运用情况,验证了支持向量机在小样本情况下比神经网络具有更强的泛化能力.     

基于LMD切片双谱和SVM轴承故障诊断研究

针对滚动轴承发生点蚀故障时峭度和1倍频能量都会发生变化的情况,提出了基于局部均值对故障信号进行分解的方法。该方法取峭度值较大有效分量信号进行重构,再做切片双谱进一步降低高斯噪声对特征频率的影响,提取故障特征1倍频、2倍频和3倍频并进行归一化处理得到特征向量。利用支持向量机对提取的特征向量进行训练与测试,从而识别故障与否及发生点蚀故障的程度。通过对实测滚动轴承振动信号的分析与诊断,验证了该方法的有效性,说明其具有良好的应用前景。     

Fault diagnosis of a mine hoist using PCA and SVM techniques

A new method based on principal component analysis (PCA) and support vector machines (SVMs) is proposed for fault diagnosis of mine hoists. PCA is used to extract the principal features associated with the gearbox. Then, with the irrelevant gearbox variables removed, the remaining gearbox, the hydraulic system and the wire rope parameters were used as input to a multi-class SVM. The SVM is first trained by using the one class-based multi-class optimization algorithm and it is then applied to fault identification. Comparison of various methods showed the PCA-SVM method successfully removed redundancy to solve the dimensionality curse. These results show that the algorithm using the RBF kernel function for the SVM had the best classification properties.     

基于决策树支持向量机模型的变压器故障诊断法

根据对变压器常见故障原因和危害程度的分析,构造了电力变压器故障诊断决策树。其判定级别为自上而下,而决策树的每一叶都对应着一种具体的故障模式,并选用不同的支持向量机单元模块作为决策树不同分支中的基本分类器,建立组合支持向量机模型,实现了对故障的多分辨识别,该方法提高了故障分析及预测的准确度。应用结果表明该系统模型是富有成效的。     

Fault diagnosis using a probability least squares support vector classification machine

Coal mines require various kinds of machinery.The fault diagnosis of this equipment has a great impact on mine production.The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine(PLSSVCM).Samples that cannot be definitely determined as belonging to one class will be assigned to a class by the PLSSVCM based on a probability value.This gives the classification results both a qualitative explanation and a quantitative evaluation.Simulation results of a fault diagnosis show that the correct rate of the PLSSVCM is 100%.Even though samples are noisy,the PLSSVCM still can effectively realize multi-class fault diagnosis of a roller bearing.The generalization property of the PLSSVCM is better than that of a neural network and a LSSVCM.     

Analog circuit intelligent fault diagnosis based on GKPCA and multi-class SVM approach

Analog circuits fault diagnosis is essential for guaranteeing the reliability and maintainability of electronic systems. In this paper, a novel analog circuit fault diagnosis approach is proposed based on greedy kernel principal component analysis (KPCA) and one-against-all support vector machine (OAASVM). In order to obtain a successful SVM-based fault classifier, eliminating noise and extracting fault features are very important. Due to the better performance of nonlinear fault features extraction and noise elimination as compared with PCA, KPCA is adopted in the proposed approach. However, when we adopt KPCA to extract fault features of analog circuit, a drawback of KPCA is that the storage required for the kernel matrix grows quadratically, and the computational cost for eigenvector of the kernel matrix grows linearly with the number of training samples. Therefore, GKPCA, which can approximate KPCA with small representation error, is introduced to enhance computational efficiency. Based on the statistical learning theory and the empirical risk minimization principle, SVM has advantages of better classification accuracy and generalization performance. The extracted fault features are then used as the inputs of OAASVM to solve fault diagnosis problem. The effectiveness of the proposed approach is verified by the experimental results.     

基于IMF信息熵与SVM的转子振动故障智能诊断方法

提出了基于内禀模态信息熵与支持向量机的转子振动故障诊断方法。采用EMD方法将转子振动信号分解为若干个内禀模态分量,由各个内禀模态分量的信息熵构成转子状态的特征参数。采用支持向量机分类器对故障类型进行分类。通过转子试验台的仿真实例证明了该方法的有效性。另外,在特征提取环节对比采用了传统的Fourier变换法,结果体现了EMD方法在非线性和非稳态信号处理上的优越性。     

基于EMD和SVM的高压断路器机械故障诊断方法研究

高压断路器操动过程中,声波信号的变化反映了断路器机械状态,声波信号特征提取直接关系到故障诊断的准确性和实用性。提出一种基于经验模态分解(empirical mode decomposition,EMD)的状态特征提取方法,根据高压断路器分闸过程的物理特点,将分闸声波信号分为分闸前期、分闸中期、分闸后期三个阶段,各阶段声波信号等时间分段,形成等时间分段能量熵以反映声波信号的时间、频率和能量变化。将能量熵输入支持向量机(support vector machine,SVM),采用"一对其余"策略进行多级分类,可诊断出断路器的正常运行状态和典型故障。通过对ZN28A型真空断路器正常状态、拐臂润滑不足、缓冲器有多余无效撞击和其它故障的测试实验,基于EMD和SVM相结合的诊断方法,有效提高了小样本下诊断断路器机械故障的准确性。     

Gear Fault Diagnosis Based on Rough Set and Support Vector Machine

By introducing Rough Set Theory and the principle of Support vector machine,a gear fault diagnosis method based on them is proposed.Firstly,diagnostic decision-making is reduced based on rough set theory,and the noise and redundancy in the sample are removed,then,according to the chosen reduction,a support vector machine multi-classifier is designed for gear fault diagnosis.Therefore,SVM'training data can be reduced and running speed can quicken.Test shows its accuracy and effi- ciency of gear fault diagnosis.     

Gear Fault Diagnosis Based on Rough Set and Support Vector Machine

By introducing Rough Set Theory and the principle of Support vector machine, a gear fault diagnosis method based on them is proposed. Firstly, diagnostic decision-making is reduced based on rough set theory, and the noise and redundancy in the sample are removed, then, according to the chosen reduction, a support vector machine multi-classifier is designed for gear fault diagnosis. Therefore, SVM' training data can be reduced and running speed can quicken. Test shows its accuracy and efficiency of gear fault diagnosis.