Artificial neural networks to classify mean shifts from multivariate χ2 chart signals

Abstract

A traditional multivariate control chart is shown to be effective in monitoring a multivariate process to signal the out-of-control condition that arises when mean shifts occur. The immediate classification of the signals associated with mean vector shifts can greatly narrow down the set of possible assignable causes, facilitating rapid analysis and corrective action by a technician before numerous nonconforming units have been manufactured. A persistent problem presented by such multivariate control charts, however, concerns the analysis of signals and the provision of any shift-related information. This study develops an artificial neural network-based model to supplement the multivariate χ2 chart. The method not only identifies the characteristic or group of characteristics that cause the signal but also classifies the magnitude of the shifts when the χ2-statistic signals that mean shifts have occurred. The method is described from the perspectives of training and classification. An example of the application of the proposed method is provided. The results demonstrate that the proposed method provides an excellent rate of classification and the output generated by trained network is very strongly correlated with the corresponding actual target value for every quality characteristic. Additionally, general guidelines for the proper implementation of the proposed method are provided.