ABSTRACT
Statistical process control charts aim to detect out-of-control signals which indicate existence of special causes effecting the process. Once such a signal is detected, the interpretation of the signal, that is, discovering the actual causes behind the signal, rests upon the shoulders of operators or engineers. Recently, some techniques have been developed for making this interpretation process easier. This study presents an overview of such techniques in three categories: traditional one-variable control charts, artificial neural network applications and
multivariate control charts.
Key Words: SPC charts, out-of-control signal, problem diagnosis
Primary Language | English |
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Journal Section | Architecture & City and Urban Planning |
Authors | |
Publication Date | August 9, 2010 |
Published in Issue | Year 2004 Volume: 17 Issue: 4 |