Multivariate statistical process monitoring and diagnosis with grouped regression‐adjusted variables
作者:
Daryl J. Hauck,
George C. Runger,
Douglas C. Montgomery,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1999)
卷期:
Volume 28,
issue 2
页码: 309-328
ISSN:0361-0918
年代: 1999
DOI:10.1080/03610919908813551
出版商: Marcel Dekker, Inc.
关键词: multivariate statistical process control;regression adjustment;control charts;quality control
数据来源: Taylor
摘要:
A common theme among the many existing multivariate statistical process monitoring (MSPM) methods is the recommendation that process knowledge be used to select a suitable monitoring procedure. Several methods possess the property of directional invariance, with shift detection performance depending only on the distance of a shift away from the target mean vector. This property is of special importance when characterizing a new process, or when available process knowledge suggests that shifts may occur in virtually any direction away from the target mean. In other cases, it is possible and may be desirable to increase a control scheme's sensitivity by using knowledge of the process structure and possible upset mechanisms to ‘aim’ the control procedure. This paper identifies a potentially common MSPM scenario and extends the idea of using process knowledge to determine an appropriate control statistic for assignable cause detection and identification.
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