| Summary: | 碩士 === 元智大學 === 工業工程與管理學系 === 93 === Quality-control problems in several related variables are called multivariate quality-control problems. Various types of quality control charts have been proposed to monitor the mean or variance of a multivariate process. Besides the use of control charts in monitoring process and identifying assignable causes, quality practitioners frequently need to adjust processes based on the magnitude of change.
In this research, we develop a control procedure based on artificial neural network to monitor the mean and variance of the multivariate process. At the same time, this procedure will predict the magnitudes of changes to provide the engineers to modify the process. The performance of the artificial neural network and traditional multivariate quality control method have been evaluated by the average run length (ARL) and mean absolute percent errors (MAPE) using simulation. The results indicate that artificial neural network has certain advantage over the traditional multivariate quality control method.
The feature of this research is that even if the magnitude of changes is unknown, the detecting ability of neural network is better than traditional multivariate CUSUM method.
|
|---|
