MONITORING AND CHARACTERIZING THE PROCESS MEAN SHIFTS BY ARTIFICIAL NEURAL NETWORKS

ABSTRACT

In statistical process control, once a control chart issues an out-of-control signal, an immediate step to identify assignable causes further is to estimate the magnitude of a specific process change. In this study, we propose a neural network-based approach to monitor the process mean shifts and to predict the magnitudes of shifts. The performances of neural networks were evaluated by estimating the average run lengths (ARL's) and mean absolute percent errors using simulation. The results obtained with simulated data suggest that neural networks outperform CUSUM charts in terms of ARL's and estimation capabilities.

Keywords:

• CUSUM
• neural networks
• process mean shifts
• magnitude of shifts
• average run lengths