基于优化多核支持向量回归的制造过程均值偏移幅度估计  

Estimation of Mean Shift Size in Manufacturing Process with Optimized Multi-kernel Support Vector Regression

作　　者：朱波[1] 刘飞[1] 

机构地区：[1]重庆大学机械传动国家重点实验室,重庆400030

出　　处：《中国机械工程》2014年第5期630-635,641,共7页China Mechanical Engineering

基　　金：国家高技术研究发展计划(863计划)资助项目(2012AA041306);国家科技重大专项(2011ZX04001-041-06)

摘　　要：为更加准确地估计制造过程均值偏移幅度,提出了一种基于多核函数支持向量回归(SVR)的估计方法。多核函数由线性核、多项式核和径向基核3种基本核函数凸组合而成,并通过粒子群优化算法(PSO)对核参数、组合权重系数以及SVR的惩罚系数C进行联合优化,以五折交叉验证求得训练样本的决定系数均值作为粒子适应度值,使生成的多核SVR获得良好的泛化能力。将该多核SVR与累积和(CUSUM)控制图集成构建了过程均值偏移监测模型,仿真实验结果表明,该方法相对人工神经网络(ANN)方法估计精度明显提高,比采用单一径向基核函数的SVR更为优越;在实际齿轮加工过程中进行应用验证,进一步证实了该方法的有效性和实用性。In order to estimate the mean shift size in manufacturing process more accurately, a multi--kernel SVR was proposed. The multiple kernel function was convex combination of three basic single kernel functions, i.e. the linear kernel, the polynomial kernel and the radial basis kernel. For constructing the multi--kernel function, PSO algorithm was adopted to optimize the kernel parameters, the weight coefficient and the punishment coefficient C of SVR jointly. And the five--fold cross validation method was applied to compute the fitness value of each particle, which was the average of determination coefficient values of SVR on each of the five groups of samples. As a result, the final multi--kernel SVR obtains good generalization ability. This multi--kernel SVR was integrated with CUSUM chart to monitor and quantify mean shifts in process. The simulation results indicate that it improves a lot on estimating mean shift size compared with the ANN method, and also is superior to SVR with single radial basis kernel. An application examination on actual gear machining process further verified its validity and practicability.

关 键 词：统计过程控制 偏移幅度 支持向量回归 多核函数 粒子群优化 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TH165.4[自动化与计算机技术—计算机科学与技术]