最小二乘支持向量机在水平井产能预测中的应用

随着油气勘探开发技术的发展,水平井以其泄油面积大、油气流动阻力小的突出优点越来越受到人们的重视。通过对水平井的产能预测,可以预测该水平井的开发效益及经济回报,因此对水平井的产能研究变得尤为重要。鉴于水平井产能影响因素的复杂性,本文运用最小二乘支持向量机技术,对多因素非线性影响下的水平井产能进行预测,并通过实例说明该方法的实用性。     

往复泵泵阀的谐波小波包分析及LS-SVM方法研究

针对往复泵泵阀冲击振动信号的非平稳特性,运用谐波小波包能量特征提取和最小二乘支持向量机(LSSVM)相结合的方法识别泵阀故障。通过对泵阀冲击振动信号进行谐波小波包分解,提取各频段谐波小波包系数的能量值,将各频段能量值组成的特征向量作为诊断模型的特征向量,输入到LS-SVM多类分类器中进行故障识别,并将谐波小波包与小波包在泵阀故障诊断中的诊断准确率进行了比较。试验结果表明将谐波小波包分解和LS-SVM相结合可以准确有效地识别泵阀故障类型。     

Modeling of machined depth in laser surface texturing of medical needles

The present paper addresses the experimental modeling of process parameters in laser surface texturing (LST) of medical needles. First, experiments were carried out based on Taguchi methodology. The laser process parameters considered during LST were the circumferential overlap, axial overlap and the overscan number. A second-order regression model of the machined depth for LST was developed based on the experimental results. Second, a predictive model for the machined depth based on least squares support vector machines (LS-SVM) with radial basis functions was constructed using the same experimental swatches. Grid search and leave-one-out cross-validation were used to determine the optimal parameters of the LS-SVM model. The comparison between the second-order regression model and the LS-SVM model was carried out. The experiments indicated that the LS-SVM model is capable of better predictions of the machined depth than the second-order regression model. The validity of the LS-SVM model has been checked through the creation of micro-channels with blended edges. It was found that the predicted profile was in a good agreement with the experimental profiles. The LS-SVM model can be used to predict machined geometry of the micro-channels on medical needles.     

Least square-support vector (LS-SVM) method for modeling of methylene blue dye adsorption using copper oxide loaded on activated carbon: Kinetic and isotherm study

A multiple linear regression (MLR) model and least square support vector regression (LS-SVM) model with principal component analysis (PCA) was used for preprocessing to predict the efficiency of methylene blue adsorption onto copper oxide nanoparticle loaded on activated carbon (CuO-NP-AC) based on experimental data set achieved in batch study. The PCA-LSSVM model indicated higher predictive capability than linear method with coefficient of determination (R²) of 0.97 and 0.92 for the training and testing data set, respectively. Firstly, the novel nanoparticles including copper oxide as low cost, non-toxic, safe and reusable adsorbent was synthesized in our laboratory with a simple and routine procedure. Subsequently, this new material properties such as surface functional group, homogeneity and pore size distribution was identified by FT-IR, SEM and BET analysis. The methylene blue (MB) removal and adsorption onto the CuO-NP-AC was investigated and the influence of variables such as initial pH and MB concentration, contact time, amount of adsorbent and pH, and temperature was investigated. The results of examination of the time on experimental adsorption data and fitting the data to conventional kinetic model show the suitability of pseudo-second order and intraparticle diffusion model. Evaluation of the experimental equilibrium data by Langmuir, Tempkin, Freundlich and Dubinin Radushkevich (D-R) isotherm explore that Langmuir is superior to other model for fitting the experimental data in term of higher correlation coefficient and lower error analysis.     

基于支持向量机的天然气管道小泄漏信号检测

针对天然气管道小泄漏信号的识别问题,提出了一种基于支持向量机的回归算法模型。首先根据香农采样定理和SCADA系统的采样频率,建立了小泄漏工况的故障样本库。然后,利用小泄漏工况下的压力波数据作为训练样本,建立了LS-SVM预测模型。最后,对小泄漏工况、正常工况和调阀工况下的压力波数据进行了信号检测。结果表明,3种工况的一步预测误差的均值分别为9.8631×10-4、0.7886和2.7400×10-2,不在同一个数量级。因此,如合理设定门限值,LS-SVM检测器就能对管道小泄漏信号实现有效的识别。     

Identification of MIMO Hammerstein models using least squares support vector machines

This paper studies a method for the identification of Hammerstein models based on least squares support vector machines (LS-SVMs). The technique allows for the determination of the memoryless static nonlinearity as well as the estimation of the model parameters of the dynamic ARX part. This is done by applying the equivalent of Bai's overparameterization method for identification of Hammerstein systems in an LS-SVM context. The SISO as well as the MIMO identification cases are elaborated. The technique can lead to significant improvements with respect to classical overparameterization methods as illustrated in a number of examples. Another important advantage is that no stringent assumptions on the nature of the nonlinearity need to be imposed except for a certain degree of smoothness.     

航空铝合金三维端铣表面粗糙度的LS-SVM控制研究

为提高加工工件的表面质量,需要有效控制加工工件表面粗糙度,因此有必要建立精度高、泛化能力强的表面粗糙度预测模型。首先基于具有位错动力学物理基础的Z-A材料本构模型,建立航空铝合金7050材料的三维端面铣削有限元仿真模型,并设计正交试验验证有限元模型的可靠性;其次建立最小二乘支持向量机(LS-SVM)预测模型,以仿真所提供的样本数据为输入,拟合铣削参数与表面粗糙度的复杂非线性关系,实现了表面粗糙度的预测,结果表明LS-SVM模型预测的相对误差不超过6%;最后基于LS-SVM表面粗糙度预测模型得出各铣削参数对表面粗糙度的影响,为生产实际提供指导。     

基于最小二乘支持向量机的绝缘子等值附盐密度预测

等值附盐密度是确定污秽等级和绘制电网污区分布图的主要依据，而绝缘子污秽在线监测系统主要监测绝缘子表面泄漏电流和环境参数。研究表明，泄漏电流除了和绝缘子表面的污秽状况有关外，还受温度、湿度等环境因素的影响，并且和各因素之间存在着复杂的非线性关系。文中在实验室模拟试验和现场实测数据基础上，利用最小二乘支持向量机，建立了以泄漏电流有效值、泄漏电流脉冲峰值、泄漏电流脉冲频度、环境湿度、温度等五个变量作为输入参数，等值附盐密度作为输出参数的智能预测模型。并通过部分实验数据验证了该方法的可行性。     

Particle swarm optimization-least squares support vector regression based forecasting model on dissolved gases in oil-filled power transformers

This paper presents a forecasting model based upon least squares support vector machine (LS-SVM) regression and particle swarm optimization (PSO) algorithm on dissolved gases in oil-filled power transformers. First, the LS-SVM regression model, with radial basis function (RBF) kernel, is established to facilitate the forecasting model. Then a global optimizer, PSO is employed to optimize the hyper-parameters needed in LS-SVM regression. Afterward, a procedure is put forward to serve as an effective tool for forecasting of gas contents in transformer oil. The application of the proposed model on actual transformer gas data has given promising results. Moreover, four other forecasting models, derived from back propagation neural network (BPNN), radial basis function neural network (RBFNN), generalized regression neural network (GRNN) and support vector regression (SVR), are selected for comparisons. The experimental results further demonstrate that the proposed model achieves better forecasting performance than its counterparts under the circumstances of limited samples.     

基于LS-SVM的电力电子电路故障预测方法

针对现有电力电子电路故障预测技术的不足,提出将电路特征性能参数和最小二乘支持向量机(least squares support vector machine,LS-SVM)预测算法结合,对电力电子电路进行故障预测.以Buck电路为例,选择电路输出电压作为监测信号,提取输出电压平均值及纹波值作为电路特征性能参数,并利用L...