基于LSSVM的多元过程非参数监控方法研究  

A Study of the Nonparametric Method for Multivariate Process Monitoring Based on LSSVM

作　　者：李莉[1] 何曙光[2] LI Li;HE Shuguang(School of Electronics&Information Engineering,Tianjin Vocational Institute,Tianjin 300410,China;College of Management&Economics,Tianjin University,Tianjin 300072,China)

机构地区：[1]天津职业大学电子信息工程学院,天津300410 [2]天津大学管理与经济学部,天津300072

出　　处：《工业工程》2020年第1期18-22,34,共6页Industrial Engineering Journal

基　　金：国家自然科学基金资助项目(71472132)

摘　　要：多元控制图常用于对多个相关变量进行监控,用以发现制造过程中存在的系统性变异。当多元过程的分布未知时,常用非参数方法进行过程监控。针对多元过程监控问题,提出了一种基于最小二乘支持向量机(least squares support vector machine,LSSVM)的多元过程非参数监控方法。在仅有受控数据(参考数据集)的条件下,采用移动窗口技术对过程数据序列进行预处理,并与参考数据集一起用于对LSSVM进行动态训练,进而以移动窗口中的数据与分类超平面之间的距离为控制变量进行多元过程监控。讨论了监控模型设计与参数选择方法并通过仿真和实例进行了性能评估。Multivariate control charts are often used to monitor process with more than one variable and detect whether there are systematic deviations in the process. When the distribution of the process variables is unknown, nonparametric methods are commonly used alternatively. To the multivariate process with no baseline distribution, a nonparametric multivariate process monitoring method is proposed based on LSSVM(least squares support vector machine). Under the situation where there are in-control data(denoted as reference dataset) only, the incoming process observations are preprocessed using the moving window method and used to dynamically train the LSSVM model together with the reference dataset. Then the distances between the samples in the moving windows and the hyperplane of the trained LSSVM are used as monitoring statistics. The design of the monitoring model and parameter selection are discussed and the performance of the model is evaluated both with simulations and with a real case.

关 键 词：多元过程监控 最小二乘支持向量机 移动窗口 非参数方法 

分 类 号：TH16[机械工程—机械制造及自动化] TG65[金属学及工艺—金属切削加工及机床]