Abstract: It is known that, under the integrated scheme of  statistical process control（SPC） and engineering process control（EPC）, the SPCs capability of monitoring the feedback-controlled process is low.To resolve this problem, neural network techniques are introduced into the integrated SPC/EPC method. Based on structural analysis and parameter setting, a three-layer neural network model is presented. For model training, the input data include process inputs, process outputs, and their covariance, and the output dada are whether an abnormality occurs. A number of tests are done to compare with Shewhart chart and CUSUM chart methods. Results show that the proposed model outperforms traditional SPC methods. It can accurately monitor a process for step disturbance with change over 2 and process drift with range over 2, and average run length(ARL) value equal to 1. While the traditional SPC methods can correctly monitor a process (monitoring rate > 90%) only for step disturbance with change over 5 and process drift with range over 2, and ARL value greater than 2.

Key words: statistical process control, engineering process control, neural networks