期刊
COMPUTERS & INDUSTRIAL ENGINEERING
卷 60, 期 2, 页码 269-278出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2010.11.009
关键词
Neural networks; Multivariate control charts; Variance shifts
Most of the research in statistical process control has been focused on monitoring the process mean. Typically, it is also important to detect variance changes as well. This paper presents a neural network-based approach for detecting bivariate process variance shifts. Some important implementation issues of neural networks are investigated, including analysis window size, number of training examples, sample size, training algorithm, etc. The performance of the neural network, in terms of the ARL and run length distribution, is compared with that of traditional multivariate control charts. Through rigorous evaluation and comparison, our research results show that the proposed neural network performs substantially better than the traditional generalized variance chart and might perform better than the adaptive sizes control charts in the case that the out-of-control covariance matrix is not known in advance. (C) 2010 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据