An integrated neural network approach for simultaneous monitoring of process mean and variance shifts a comparative study

Abstract

Diagnosis of assignable causes is one of the primary concerns of quality practitioners. Knowledge of the type of process shift, either due to process mean or variance, can greatly aid identification of assignable causes. However, properties of control charts used for monitoring process mean and variance simultaneously are seldom studied in the statistical process control (SPC) literature. In this study, we propose a combined neural network control scheme to compare with other traditional SPC charts, such as X and R , CUSM and EWMA charts, and other neural network and Bayesian classification techniques in terms of average run length (ARL), and percentage of correct classifications. With an extensive literature survey and computer simulations, we find that the proposed neural network control scheme outperforms other SPC charts in the majority of situations for individual observations and a subgroup sample size of five.