Abstract: In order to further improve the efficiency of autocorrelation process monitoring, the residual control chart for autocorrelation process using gated recurrent unit (GRU) neural network is proposed. The GRU network is off-line trained and tested with the autocorrelation process data in control to monitor the prediction error and form the residual control chart for control. The trained GRU network is used to predict the current process variation and the residual control chart is used to determine whether the current process is out of control. Monte Carlo simulation method is used to compare the performance with the residual control chart based on first-order autoregressive model, BP neural network and support vector regression. The experiment results indicate that the difference of ARL between the proposed residual control chart and the other three kinds of control charts is small, that is, the performance of the four kinds of control charts is equivalent when the process is in control; while in the process of abnormal mean shift, the ARL of the proposed residual control chart is smaller than the other three kinds of control charts. The monitoring efficiency of the residual chart presented in this study has a remarkable improvement for mean shift of autocorrelation process.

Key words: autocorrelated process, deep learning, gated recurrent unit neural network, control chart, statistical process control