支持向量回归机在数控加工中心热误差建模中的应用

研究并选择最佳模型对数控加工中心加工过程中的主要误差源-主轴热误差进行补偿,以便提高机床的加工精度.以leaderway-V450加工中心为实验对象,对主轴热误差支持向量回归机模型和多元回归模型进行了分析对比.首先,根据夏季数据建立了多元回归模型和支持向量回归机模型.然后,将夏季另一批数据和秋季数据分别代入两种模型计算各模型补偿精度.最后,根据两种模型的精度变化规律比较两者稳健性.实验结果表明:支持向量回归机夏季模型用于补偿夏季和秋季热误差补偿标准差都小于2 μm,而多元回归模型用于补偿夏季数据补偿标准差小于2μm,用于补偿秋季数据补偿标准差大于8μm.数据显示支持向量回归机模型用于热误差补偿不仅具有较高精度,同时具有较好鲁棒性.     

最速上升关联向量机高光谱影像分类

针对高光谱影像近邻波段高度相关,直接在高维空间分类并非最优的问题,提出了基于最速上升和关联向量机(SA-RVM)的高光谱影像分类算法.使用最速上升(SA)算法搜索最优特征子空间,剔除冗余特征;然后,在特征子空间中训练RVM并分类.对4套测试数据进行的实验表明,SA选择的特征子空间中,RVM分类精度提高了2.5％以上,与支持向量机(SVM)相当.对训练样本较少的2套数据,精度提高了5.63％和6.2％.此外,SA-RVM的解稀疏,预测未知样本类别属性所需时间短.总体来看,SA-RVM精度高、判别速度快,适合处理大场景高光谱影像.     

基于支持向量机回归机的曲面拟合技术

提出了一种基于支持向量回归的点云曲面重构方法,从点云中按一定规则取样得到小样本集,以小样本集为支持向量,并以径向基函数为核函数重建复杂线性函数曲面模型。实验表明该方法能直接重建散乱点云数据．拟合出曲面模型且具有较好的效果,并具有误差小、速度快等优点。     

基于支持向量机工具的性能劣化建模方法

针对基于故障数据的数控装备可靠性研究中的小样本问题,提出了建立基于支持向量机的性能劣化模型.在研究支持向量机的建模理论和参数优化方法的基础上,将最小二乘法支持向量机工具LSSVM.M应用于性能退化数据处理,提出一种改进的参数选择方法,以提高拟合和预测准确性.通过实例,验证了该方法的可行性,并建立了数控机床加工精度的性能劣化模型,为可靠性评估奠定了基础.     

基于支持向量回归机的点云曲面重构方法

提出了一种基于支持向量回归的点云曲面重构方法,并以径向基函数为核函数重建复杂线性函数曲面模型,实验表明该方法能直接重建散乱点数据,具有误差小,速度快等优点.     

数控机床滚珠丝杠副性能退化评估技术

为准确评估滚珠丝杠副性能的退化程度,提出基于量子遗传算法和灰色神经网络的滚珠丝杠副性能退化评估方法。以CINCINNATIV5-3000加工中心的滚珠丝杠副为研究对象,设计了丝杠在线监测系统,利用动态聚类数据处理技术对采集的海量数据进行预处理,提取信号的时域、频域及时频域特征,通过主分量分析方法压缩特征数量,构建了丝杠振动信号特征向量,采用量子遗传算法优化灰色神经网络的初始化参数,将特征向量输入到灰色神经网络进行训练,进而得到丝杠性能退化模型。实践运行结果表明,所建立的丝杠性能退化模型能够有效评估数控机床的丝杠的性能,研究成果具有重要的工业推广价值。     

Application of grey model for machine degradation prognostics

Predicting machine degradation before final failure occurs is very important. This paper presents a method to predict the future state of machine degradation based on grey model and one-step-ahead forecasting technique. Specifically, the feasibility of grey model as a predictor for machine degradation prognostics system has been investigated. Grey model GM(1,1) has employed to forecast the future state of machine degradation, but the result is not satisfactory. Finally, a modification of GM(1,1) has made to improve the accuracy of prediction. However the model was built by using only four input data, it is able to track closely the sudden change of machine degradation condition. Real trending data of low methane compressor acquired from condition monitoring routine are employed for evaluating the proposed method.     

基于RVM的多功能自确认水质检测传感器

提出了一种多功能自确认水质检测传感器功能模型,可以同时测量水的温度、盐度和pH值,并可对自身工作状态进行自确认.提出了一种基于相关向量机的多功能自确认传感器故障诊断和数据恢复方法,在二分类机基础上利用层次扩展方法得到基于相关向量机的多分类机,对传感器进行故障诊断;利用水质检测过程中多个参数之间的相关信息,解决了非线性、小样本条件下的传感器故障数据恢复问题.构建了多功能自确认水质检测传感器实验平台,实验结果表明故障诊断识别率达到98％,数据恢复相对误差在士4％以内,提高了传感器的可靠性.     

基于支持向量回归的放大器性能评价研究

本文提出了基于支持向量回归(SVR)的放大器性能评价方法.确定放大器性能评价系统结构,以带通滤波放大器为实验研究对象.首先通过幅频特性测试仪(型号E4403B、规格1 Hz～3 GHz)采样,得到带通滤波器幅频特性数据集,然后进行SVR回归,得到带通滤波放大器幅频特性曲线的逼近函数,用该函数对性能指标中的4项参数进行测定.实验表明,该方法提高了参数测量的精度,适于用示波法测量电子产品性能的评价.     

基于支持向量回归机的机翼盒段结构健康监测研究

针对大型飞行器结构的健康监测问题,提出采用光纤光栅型智能结构监测方法对机翼盒段进行载荷监测.对埋置的光纤光栅传感器的光谱进行了分析,研究了光纤光栅传感器中心波长变化与载荷位置的关系.采用支持向量机研究机翼盒段载荷自诊断问题,并对支持向量机的相关调整参数进行了优化,预测结果与广义回归神经网络进行对比.结果表明,支持向量机的网络测试误差为0.23%,预测精度明显高于广义回归神经网络.实验证明该方法在训练样本较少时,仍然能有效地判定载荷位置, 系统的辨识力较高.     

Bayesian degradation assessment of CNC machine tools considering unit non-homogeneity

Field reliability assessment and prediction is critical for the estimation, operation and health management of CNC machine tools. The classical methods for field reliability of CNC Machine Tools assessment and prediction are challenged with the issues of expensive reliability tests, small sample size and unit non-homogeneity. In order to solve these problems, this paper introduces a degradation analysis based reliability assessment method for CNC machine tools under performance testing. Since the degradation is an independent increment process, the gamma process is employed to characterize the degradation process of CNC machine tools. The random effects are introduced to accommodate performance degradation model with unit non-homogeneity. The parameters of model are updated by Bayesian estimation approach. As a case study, the CNC Machine Tools is studied to illustrate the approach. And the proposed method is demonstrated precise for practical use.     

磷虾群算法优化支持向量机的威胁估计

为提高目标威胁估计的预测精度,在传统支持向量机优化方法的基础上,提出了采用磷虾群算法优化支持向量机的威胁估计方法。介绍了磷虾群算法和支持向量机的原理,并基于此采用磷虾群算法对支持向量机中的惩罚参数和核函数参数进行优化,寻找最优的惩罚参数和核函数参数;建立磷虾群优化支持向量机的目标威胁估计模型,并实现基于该模型的目标威胁估计算法。采集90组原始数据组成训练集、30组数据组成测试集,对该目标威胁估计算法进行仿真实验。实验结果显示,磷虾群算法优化支持向量机的预测误差为0.002 91,小于采用粒子群算法或萤火虫算法优化的支持向量机。结果表明,磷虾群优化支持向量机的目标威胁估计方法可以有效地完成目标威胁估计。     

基于ACO和RVM的两相流流型特征选择方法

为提高流型识别的准确率,提出了基于蚁群算法(ant colony optimization,ACO)和相关向量机(relevance vector machine,RVM)封装模式的流型特征选择方法.首先采用小波包变换(wavelet packet transform,wPT)、经验模式分解方法(empirical mode decomposition,EMD)对原始压差波动信号进行分解,分别提取压差波动信号的时域无量纲指标和各分解信号的能量和熵组成融合特征.然后采用ACO和RVM进行特征选择和识别,选出有利于流型识别的特征优化组合.空气-水两相流型识别的实验结果表明:该方法能实现流型特征的有效缩减,经优化组合的最优特征子集识别率达95％以上,与其他方法相比具有更高的识别率.     

Feature selection by merging sequential bidirectional search into relevance vector machine in condition monitoring

For more accurate fault detection and diagnosis, there is an increasing trend to use a large number of sensors and to collect data at high frequency. This inevitably produces large-scale data and causes difficulties in fault classification. Actually, the classification methods are simply intractable when applied to high-dimensional condition monitoring data. In order to solve the problem, engineers have to resort to complicated feature extraction methods to reduce the dimensionality of data. However, the features transformed by the methods cannot be understood by the engineers due to a loss of the original engineering meaning. In this paper, other forms of dimensionality reduction technique(feature selection methods) are employed to identify machinery condition, based only on frequency spectrum data. Feature selection methods are usually divided into three main types: filter, wrapper and embedded methods. Most studies are mainly focused on the first two types, whilst the development and application of the embedded feature selection methods are very limited. This paper attempts to explore a novel embedded method. The method is formed by merging a sequential bidirectional search algorithm into scale parameters tuning within a kernel function in the relevance vector machine. To demonstrate the potential for applying the method to machinery fault diagnosis, the method is implemented to rolling bearing experimental data. The results obtained by using the method are consistent with the theoretical interpretation, proving that this algorithm has important engineering significance in revealing the correlation between the faults and relevant frequency features. The proposed method is a theoretical extension of relevance vector machine, and provides an effective solution to detect the fault-related frequency components with high efficiency.     

支持向量机在高压直流输电系统故障诊断中的应用

高压直流输电(HVDC)是一种新型输电技术,为更有效地诊断HVDC系统故障,本文首先对系统几种常见故障进行仿真研究,在此基础上提出将支持向量机(SVM)用于系统故障分类,并对不同参数下的SVM模型性能进行比较.研究结果表明SVM用于HVDC系统故障诊断是合理、有效的.     

Generalized linear mixed model for monitoring autocorrelated logistic regression profiles

Profile monitoring is used to monitor the regression relationship between a response variable and one or more explanatory variables over time. Many researches have been done in this area, but in most of them, the distribution of the response variable is assumed to be normal. However, this assumption is violated in many real case problems. In these instances, classic methods cannot be used for monitoring the profiles. For example, when the response variable is binary, logistic regression methods should be used rather than ordinary least square or other classic regression methods. There are some methods for monitoring logistic profiles in the literature, but the basic assumption of these methods is the independency of the consecutive observations, while this assumption is violated in some instances for example when the successive samples are taken in short intervals. This paper considers the effect of autocorrelation presence between the observations in different levels of the independent variable in a logistic regression profile on the monitoring procedure (T ² control chart) and proposes two remedies to account for the autocorrelation within logistic profiles. In one of the remedies, upper control limit of the traditional T ² control chart is modified. In the second one, we use a generalized linear mixed model (GLMM) to estimate the regression parameters and then use the T ² control chart for monitoring autocorrelated logistic regression profiles. Simulation studies show the better performance of T ² control chart when the regression parameters are estimated by the GLMM method under both step shifts and drifts.     

基于粗糙集的多项logistic回归模型在油层识别中的应用

油气水层数据统计是一种非线性分类统计问题,由此建立了多项logistic回归油气水层模式识别模型。表征油气水层各因素之间存在着复杂的耦合关系,采用粗糙集属性约简算法对原始样本数据进行属性约简,消除因素间的耦合关系对识别结果的影响。选取大庆油田某地区的20口油井的数据作为建模样本数据,另10口油井的数据为测试样本数据,实验表明基于粗糙集的多项logistic回归模型对建模样本的解释正确率为100%,对测试样本的解释正确率为90%,远高于非属性约简的多项logistic回归模型,为油气水层模式识别提供了一种新方法。     

最小二乘支向量机在焊缝跟踪中的应用

为了克服传统焊缝跟踪方法精度低等问题,采用最小二乘支持向量回归机( LSSVM)进行焊缝跟踪.最小二乘支持向量机通过构造回归函数解决焊缝跟踪问题.与支持向量机不同的是,最小二乘支持向量机通过构造一个新二次损失函数,将支持向量回归机的二次规划问题转变为求解线性方程,从而改进了原支持向量机的跟踪精度.为验证所设计控制器的有效性,进行了焊缝的跟踪实验,并设计了实验条件;实验结果表明基于LSSVM的焊缝跟踪误差小于径向基(RBF)神经网络,可见采用LSSVM的控制更能够适应实际焊接过程的变化.     

支持向量机分类与回归联合建模方法及其在拉曼光谱分析中的应用

多元校正分析模型的精度不仅依赖于模型的结构和参数,还很大程度上取决于训练样本的分布。实际过程中,训练样本通常呈现不均匀分布,导致基于全体样本的回归模型预测性能不理想。本文针对该问题提出了支持向量机分类与回归联合建模方法:首先使用最小二乘支持向量机(LS-SVM)分类器构建分类决策树,然后对每一类样本分别建立最小二乘支持向量机回归模型;对未知样本进行定量分析时,首先经过分类决策树分类,再根据分类信息选择相应的回归模型进行计算。针对汽油辛烷值拉曼光谱分析问题,基于全体样本建模的LS-SVM回归模型的标准预测误差为0.54,而采用本文方法所得的模型预测误差为0.22,大幅度地提高了分析精度。