Shifts recognition in correlated process data using a neural network

Traditional statistical process control (SPC) techniques of control charting are not applicable in many process industries because data from these facilities are autocorrelated. Therefore the reduction in process variability obtained through the use of SPC techniques has not been realized in process industries. Techniques are needed to serve the same functions as SPC control charts, which are to identify shifts in correlated parameters. Neural networks are a potential tool for identifying shifts in correlated process parameters, as data independence is not an assumption of neural network theory. In this research, a back-propagation neural network is utilized to identify shifts in process parameter values from AR(1) time series models with varying values of the autocorrelation coefficient φ. To find the appropriate number of input nodes for use in a neural network model, the all-possible-regression selection procedure is applied. In addition, time series residual control charts are also developed for the data sets for comparison. As the results reveal, networks were successful at separating data that were shifted one, two and three standard deviations from non-shifted data for generated process data. The SPC control charts were not able to identify the same process shifts. In the other words, the neural networks can be used to identify shifts in process parameters. Therefore, it is allowing improved control in manufacturing processes that generate correlated process data.