Recognition and classification of single and concurrent unnatural patterns in control charts via neural networks and fitted line of samples

Abstract

The correct, prompt recognition and analysis of unnatural and significant patterns in Schewhart’s control charts are very important since they remind out-of-control conditions. In fact, pattern extraction increases the sensitivity of charts when identifying out of control conditions. Artificial neural networks have been used to identify unnatural patterns in many research studies due to their high efficiency in pattern recognition. In most of such studies, there is a significant risk of misclassification of highly sensitive patterns. To put it more clearly, the proposed models offered for the recognition of patterns with low parametric coefficients are mistaken. This study, offers a model for the recognition and analysis of basic patterns in process control charts using LVQ and MLP networks along with a fitted line analysis. In this model, not only does risk of misclassification at different levels of sensitivity decrease remarkably, but there will also be the possibility for recognition and analysis when basic pattern occur simultaneously. The efficiency and effectiveness of the model are shown by conducting tests based on simulation.