工业工程

工业工程 ›› 2013, Vol. 16 ›› Issue (1): 132-.

• 专题论述 • 上一篇    

基于支持向量数据描述的指数加权移动平均控制图

  

  1. (天津大学 管理与经济学部，天津 300072)
  • 出版日期:2013-02-28 发布日期:2013-03-22
  • 作者简介:何曙光（1975-），男，内蒙古自治区人，教授，博士，主要研究方向为现代工业工程理论与应用.
  • 基金资助:

    国家自然科学基金资助项目(71002105)

Support Vector Data Description-based Multivariate Exponentially Weighted Moving Average Control Chart

  1. (School of Management, Tianjin University, Tianjin 300072, China) 
  • Online:2013-02-28 Published:2013-03-22

摘要： 基于支持向量数据描述(Support Vector Data Description，SVDD)的D2控制图的重要特点是对过程数据的抽样分布没有特定的要求。由于D2控制图仅使用当前观测点的值来计算统计量，对过程的小偏移并不敏感，在SVDD模型的基础上，提出了基于D2距离的多元加权移动平均(multivariate exponentially weighted moving average，MEWMA)控制图，用S-MEWMA表示。仿真结果表明，无论过程数据服从正态分布还是非正态分布，S-MEWMA控制图均优于 D2控制图。

关键词: 支持向量数据描述, MEWMA控制图, 多元过程控制

Abstract: The D2 control chart based on support vector data description (SVDD) has an advantage that it does not require a known sampling distribution for the process data. However, it uses only the current samples, leading to that it is insensitive to small shifts. To solve this problem, based on SVDD method, a multivariate exponentially weighted moving average (MEWMA) control chart (denoted as S-MEWMA) is proposed in this paper. Simulation results show that the S-MEWMA chart outperforms the D2 control chart no matter whether a process follows a normal or non-normal distribution.

Key words: support vector data description, multivariate exponentially weighted moving average(MEWMA) control chart, multivariate statistical process control