面向未知自相关过程的Bootstrap控制图设计

doi:10.3969/j.issn.1007-7375.2018.04.004

工业工程 ›› 2018, Vol. 21 ›› Issue (4): 23-33.doi: 10.3969/j.issn.1007-7375.2018.04.004

面向未知自相关过程的Bootstrap控制图设计

娄璐, 李艳婷

上海交通大学工业工程与管理系, 上海 200240

收稿日期:2017-11-20 出版日期:2018-08-30 发布日期:2018-08-27
作者简介:娄璐(1992-),女,贵州省人,硕士研究生,主要研究方向为质量管理、多工序制造与服务过程质量控制与诊断.
基金资助:
国家自然科学基金资助项目（71672109）

Bootstrap-Based Control Chart Design for Unknown Autocorrelated Processes

LOU Lu, LI Yanting

Department of Industrial Engineering & Management, Shanghai Jiaotong University, Shanghai 200240, China

Received:2017-11-20 Online:2018-08-30 Published:2018-08-27

摘要/Abstract

摘要： 针对自动化生产中，通过拟合自回归滑动平均模型(ARMA)建立残差控制图监控未知自相关过程数据时，存在误报率高的问题，提出一种基于Bootstrap的方法，通过重构样本，对原始数据建立非参数控制图。在考虑不同的模型系数、偏移大小、样本个数及残差分布类型的情况下，通过蒙特卡洛模拟，比较传统残差控制图和新控制图的平均运行链长(ARL)，证明新控制图提高了对过程偏移的灵敏度，降低了误报率。实际应用中，新的Bootstrap控制图在仅获取一组Phase-I阶段的受控数据样本下即可生成，受所取样本个数的影响较小，且直接用于监控原始数据，适用范围广，操作简便。

Abstract: The shortcoming of applying a traditional control chart to the residuals of ARMA (auto-regressive and moving average) model estimated from process observation is analyzed, and an improved nonparametric control chart based on bootstrap resampling is presented. Average run length(ARL) considering various factors including model parameters, number of samples and distribution of residuals, are compared by Monte Carlo simulation. The results show the new control chart increases sensitivity to process shifts, and reduces false alarm rates. While bootstrap-based control chart can be built when a set of Phase-I in-control data are given and applied to raw data directly, the control effect is less affected by the number of samples, and so the proposed method is powerful yet simple to use in practice.

Key words: autocorrelated processes, model-unknown, phase-I, Bootstrap, Motnte Carlo simulation, average run length(ARL)

中图分类号:

TP277

娄璐, 李艳婷. 面向未知自相关过程的Bootstrap控制图设计[J]. 工业工程, 2018, 21(4): 23-33.

LOU Lu, LI Yanting. Bootstrap-Based Control Chart Design for Unknown Autocorrelated Processes[J]. Industrial Engineering Journal , 2018, 21(4): 23-33.

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面向未知自相关过程的Bootstrap控制图设计

Bootstrap-Based Control Chart Design for Unknown Autocorrelated Processes

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