A self-starting control chart for high-dimensional short-run processes

被引:24
|
作者
Li, Yanting [1 ]
Liu, Yukun [2 ]
Zou, Changliang [3 ]
Jiang, Wei [4 ]
机构
[1] Shanghai Jiao Tong Univ, Dept Ind Engn & Management, Shanghai 200030, Peoples R China
[2] E China Normal Univ, Sch Finance & Stat, Shanghai 200062, Peoples R China
[3] Nankai Univ, Dept Stat, Tianjin 300071, Peoples R China
[4] Shanghai Jiao Tong Univ, Antai Sch Management, Shanghai 200030, Peoples R China
基金
中国国家自然科学基金;
关键词
high-dimensional observations; short-run processes; mean shift; control chart; average run length; STATISTICAL PROCESS-CONTROL; CHANGE-POINT MODEL; MEAN VECTOR; VARIANCE; SHIFTS;
D O I
10.1080/00207543.2013.832001
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A key challenge in using a traditional Hotelling's T-2 chart with high-dimensionality measurements is that monitoring cannot begin until after the number of observations exceeds the dimensionality of the measurements, and the detection sensitivity to early shifts is reduced after that point until a substantial amount of observations has been accumulated. This is especially important with short-run processes where the measurements have high dimensionality. This article proposes a chart that allows monitoring with the second observation regardless of the dimensionality and reduces the average run length in detecting early shifts in high-dimensionality measurements. The proposed control chart can start monitoring quite early before considerable reference data are collected during the initial stage of production. A change point estimate is also available from our procedure, which is shown consistent for locating the correct change point. Both simulation results and an industry example show the effectiveness of the proposed control chart for monitoring short-run processes with high dimensionality.
引用
收藏
页码:445 / 461
页数:17
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