A Neural Network Approach to Process Quality Control in the Presence of Serial Correlation

• Main Authors: Tsung-Hung Wu, 吳聰宏
• Other Authors: Chuen-Sheng Cheng
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/78436116581231408076

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.