A Neural Network Approach to Process Quality Control in the Presence of Serial Correlation
碩士 === 元智大學 === 工業工程研究所 === 82 === Control charts are an important tool in statistical process control. Control chart analysis is based on the assumption that process data are normally and independently distributed. But in the real world, the data measure...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Online Access: | http://ndltd.ncl.edu.tw/handle/78436116581231408076 |
| Summary: | 碩士 === 元智大學 === 工業工程研究所 === 82 === Control charts are an important tool in statistical process
control. Control chart analysis is based on the assumption that
process data are normally and independently distributed. But in
the real world, the data measured from industrial process are
often serial correlated. It will lead to many false alarms if
the users neglect the correlation structure of process data. In
the past, traditional control methods are applied to the
uncorrelated residuals of a time series model. It has been
shown that this approach may be feasible but not effective. In
this research, a control method based on artificial neural
networks techniques has been developed as an alternative to
monitor serial-correlated data. Simulation results show that
the proposed approach is more effective comparing to Shewhart-
CUSUM control schemes in detecting small to medium process
changes.
|
|---|