An Artifical Neural Networks Based Model to Indentify Faults in Mulivariate Quality Control Charts
نویسنده
چکیده
Most multivariate quality control procedures evaluate the in-control or out-of-control condition based upon an overall statistic, like Hotelling’s T. Although T 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, which 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 is used. The results of the model implementation on a couple of numerical examples are encouraging.
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