Integrating Statistical Process Control and Engineering Process Control
作者:
MontgomeryDouglas C.,
KeatsJ. Bert,
RungerGeorge C.,
MessinaWilliam S.,
期刊:
Journal of Quality Technology
(Taylor Available online 1994)
卷期:
Volume 26,
issue 2
页码: 79-87
ISSN:0022-4065
年代: 1994
DOI:10.1080/00224065.1994.11979508
出版商: Taylor&Francis
关键词: Autoregressive Integrated Moving Average Process;Engineering Process Control;Minimum Mean Square Error Controller;Statistical Process Control
数据来源: Taylor
摘要:
Statistical process control (SPC) is traditionally applied to processes that vary about a fixed mean, and where successive observations are viewed as statistically independent. Engineering process control (EPC) is usually applied to processes in which successive observations are related over time, and where the mean drifts dynamically. Thus, EPC seeks to minimize variability by transferring it from the output variable to a related process input (controllable) variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. This paper shows through simulation that when using EPC it is always better to have an SPC system in place that monitors and acts properly on the root cause of the assignable change.
点击下载:
PDF (697KB)
返 回