Application of artificial neural networks in linear profile monitoring

► Three ANN based methods for monitoring linear profiles are proposed. ► Proposed methods perform better than T2, EWMA/R and EWMA-3 charts for medium to large shifts. ► A advantage of proposed ANN methods is to train the neural networks to detect desired shifts. ► ARL criterion is used to assess the efficiencies of the methods. ► Control charts are used to detect shifts in intercept, slope and residuals. In many quality control applications the quality of process or product is characterized and summarized by a relation (profile) between a response variable and one or more explanatory variables. Such profiles can be modeled using linear or nonlinear regression models. In this paper we use artificial neural networks to detect and classify the shifts in linear profiles. Three monitoring methods based on artificial neural networks are developed to monitor linear profiles. Their efficacies are assessed using average run length criterion.