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.%为解决统计过程控制(SPC)/工程过程调整(EPC)整合引起的传统SPC控制图监测异常扰动效率低的问题,提出了采用神经网络技术监测SPC/EPC整合过程的策略,并对神经网络模型结构和参数设置进行分析,构建过程输入、过程输出及两者的协方差为输人参数,异常扰动发生与否为输出参数的3层神经网络模型.为验证该方法的性能,进行了大量的比较实验:即对相同的样本,分别采用Shewhar图、CUSUM图和上述神经网络模型进行监测.实验结果表明:神经网络模型能准确监测幅度大于2的阶跃扰动和大于2的过程漂移,平均运行步长(ARL)为1；传统SPC监测技术只能较准确地(监测率大于90％)监测幅度大于5的阶跃扰动和大于2的过程漂移,ARL大于2.与传统监测方法相比,该方法能快速有效地监测异常扰动的发生.
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