An effective multivariate control chart for detecting small mean shifts using support vector data description

作     者：Xia, Beixin Jian, Zheng Liu, Lei Li, Long 

作者机构：Shanghai Univ Sch Management Shanghai Peoples R China Shanghai Univ Sch Mechatron Engn & Automat 149 Yanchang Rd Shanghai 200072 Peoples R China Apple Inc Cupertino CA USA 

出 版 物：《ADVANCES IN MECHANICAL ENGINEERING》 (机械工程进展)

年 卷 期：2018年第10卷第11期

核心收录：

学科分类：08[工学] 0807[工学-动力工程及工程热物理] 0802[工学-机械工程] 

基　　金：National Natural Science Foundation of China 

主　　题：Multivariate statistical process control multivariate cumulative sum chart support vector data description average run length quality control 

摘      要：Conventional multivariate cumulative sum control charts are more sensitive to small shifts than T2 control charts, but they cannot get the knowledge of manufacturing process through the learning of in-control data due to the characteristics of their own structures. To address this issue, a modified multivariate cumulative sum control chart based on support vector data description for multivariate statistical process control is proposed in this article, which is named D-MCUSUM control chart. The proposed control chart will have both advantages of the multivariate cumulative sum control charts and the support vector data description algorithm, namely, high sensitivities to small shifts and learning abilities. The recommended values of some key parameters are also given for a better application. Based on these, a bivariate simulation experiment is conducted to evaluate the performance of the D-MCUSUM control chart. A real industrial case illustrates the application of the proposed control chart. The results also show that the D-MCUSUM control chart is more sensitive to small shifts than other traditional control charts (e.g. T2 and multivariate cumulative sum) and a D control chart based on support vector data description.