MONITORING MULTIVARIATE ENVIRONMENTS USING ARTIFICIAL NEURAL NETWORK APPROACH: AN OVERVIEW

Abstract. When a process shifts to an out-of-control condition, a search should be initiated to identify and eliminate the special cause(s) manifested to the technical specication(s) of the process. In the case of a process (or a product) involving several correlated technical specications, analyzing the joint eects of the correlated specications is more complicated compared to a process involving only one technical specication. Most real cases refer to processes involving more than one variable. The complexity of a solution to monitor the condition of these processes, estimate the change point and identify further knowledge leading to root-cause analysis motivated researchers to develop solutions based on Articial Neural Networks (ANN). This paper provides, analytically, a comprehensive literature review on monitoring multivariate processes approaching articial neural networks. Analysis of the strength and weakness of the proposed schemes, along with comparing their capabilities and properties,, are also considered. Some opportunities for new researches into monitoring multivariate environments are provided in this paper.