基于神经网络的SPC/EPC整合过程监测方法研究  

Method of Monitoring Abnormality under Integrated SPC/EPC Based on Neural Network Techniques

作　　者：王秀红[1] 

机构地区：[1]郑州航空工业管理学院管理科学与工程学院,河南郑州450015

出　　处：《工业工程》2012年第4期12-16,共5页Industrial Engineering Journal

基　　金：国家自然科学基金资助项目(70771102);航空科学基金资助项目(2010ZG55025);河南省科技攻关资助项目(122102210512)

摘　　要：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入参数,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。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.

关 键 词：统计过程控制 工程过程控制 神经网络 

分 类 号：TB114.2[理学—运筹学与控制论]