%0 Journal Article
%T 基于支持向量机的混杂数据过程控制<br>Process Control of Mixed Data Based on Support Vector Machine
%A 孙静
%A 郭叙成
%J Dynamical Systems and Control
%P 1-10
%@ 2325-6761
%D 2022
%I Hans Publishing
%R 10.12677/DSC.2022.111001
%X 近年来，随着制造系统智能化和复杂化程度提升，获取的数据呈现出数据来源多元化、数据量激增、数据类型混杂等特点。本文研究了计量数据(measurable data)与属性数据(attribute data)的混杂数据过程控制与诊断问题。借助支持向量机(Support Vector Machine, SVM)，探讨了多元混杂数据出现均值偏移时的过程控制与诊断。沿着从多元正态数据到混杂数据的研究思路，探讨了SVM准确率和支持向量个数，在偏移量、子组大小和相关系数变化的情况下，对比四类核函数的SVM的过程控制能力；采用二分类SVM和多分类M-SVM，分析了过程控制和过程诊断能力；并与Hotelling多元控制图进行性能比较。研究发现，利用SVM可以有效实现混杂数据的过程控制和诊断。利用SVM对混杂数据进行过程控制，其性能远优于经典的Hotelling多元控制图。<br />
Recently, as manufacturing system is becoming increasingly automated and intelligent, the data obtained from manufacturing system also presents the characteristics of diversified data sources, large data volumes, and mixed data types. In this paper, it is discussed on process control and diagnoses of mixed data in order to handle both measurable data and attribute data simultaneously. Support Vector Machine (SVM) is applied to realize the process control and diagnosis for multivariate mixed data when the process mean out of control. From multivariate normal data to mixed data, the accuracy of SVM is studied from the viewpoints of the shift, subgroup size, and the change of correlations to make comparison among four different kernels, and then C-SVM and M-SVM are provided to monitor the process in control or not and identify which parameter(s) to be out of control. Furthermore, compared SVM with Hotelling’s multivariate control chart, it is concluded that SVM has excellent performance on monitoring the process in control or not when it is applied to mixed data.
%K 混杂数据，多元过程控制，支持向量机<br>Mixed Data
%K Multivariate Process Control
%K Support Vector Machine
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=48035