期刊
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
卷 21, 期 8, 页码 825-840出版社
JOHN WILEY & SONS LTD
DOI: 10.1002/qre.689
关键词
statistical process control; multivariate control charts; artificial neural networks
Most multivariate quality control procedures evaluate the in-control or out-of-control condition based upon an overall statistic, like Hotelling's T-2. Although T-2 is optimal for finding a general shift in mean vectors, it is not optimal for shifts that occur for some subset of variables. This introduces a persistent problem in multivariate control charts, namely the interpretation of a signal that often discourages practitioners in applying them. In this paper, we propose an artificial neural network based model to diagnose faults in out-of-control conditions and to help identify aberrant variables when Shewhart-type multivariate control charts based on Hotelling's T-2 are used. The results of the model implementation on two numerical examples and one case of real world data are encouraging. Copyright (c) 2005 John Wiley & Sons, Ltd.
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