INTEGRATING STATISTICAL PROCESS-CONTROL AND ENGINEERING PROCESS-CONTROL

被引:127
|
作者
MONTGOMERY, DC
KEATS, JB
RUNGER, GC
MESSINA, WS
机构
[1] INTERACT QUAL,TUCSON,AZ 85715
[2] WALSH AMER,SALES & MKT RES,PHOENIX,AZ 85299
[3] ARIZONA STATE UNIV,STAT & ENGN APPLICAT QUAL LAB,TEMPE,AZ 85287
关键词
AUTOREGRESSIVE INTEGRATED MOVING AVERAGE PROCESS; ENGINEERING PROCESS CONTROL; MINIMUM MEAN SQUARE ERROR CONTROLLER; STATISTICAL PROCESS CONTROL;
D O I
10.1080/00224065.1994.11979508
中图分类号
T [工业技术];
学科分类号
08 ;
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.
引用
收藏
页码:79 / 87
页数:9
相关论文
共 50 条