Control charts for monitoring autocorrelated processes based on Neural Networks model

Statistical process control can have different objectives and can be done in different forms (Hawkins, et al, 2003). Currently, considerable attention has been given to the effect of data correlation on the statistical process control (SPC). The use of traditional SPC methods when observations are correlated often leads to misleading conclusions as to whether or not the process is under control. This paper presents the construction of residual based control charts, obtained from neural network model, to monitor the mean and dispersion in autocorrelated productive processes. One application with real data and a performance comparison of the residual control charts obtained from the artificial neural network model with that of traditional control charts X(bar) and R presented. It is established that the former procedure is more efficient in detecting changes in the mean and dispersion of the process than the latter.