Detection and classification mean-shifts in multi-attribute processes by artificial neural networks

To monitor the quality of a multi-attribute process, some issues arise. One of them being the occurrence of a high number of false alarms (type I error) and the other an increase in the probability of not detecting defects when the process is monitored by a set of independent uni-attribute control charts. In this paper, based upon the artificial neural network capabilities we develop a new methodology to overcome this problem. We design a perceptron neural network to monitor either the proportions of several types of product nonconformities (instead of using several np charts) or the number of different types of defects (instead of using several c charts) in a product. Moreover, while the proposed method possesses the ability to be applied for small sample sizes, it is also able to diagnose the mean shift online. We present two simulation experiments in which the proportions of several types of nonconformities are monitored. In addition, we present one more simulation experiment in which the number of different types of defect is controlled. We also compare the performance of the proposed methodology with the ones from the Mnp and T2 charts for multi-attribute processes. The results of the simulation studies are encouraging.