Title: Using neural networks for mean shift identification and magnitude of bivariate autocorrelated processes
Authors: Aikaterini Fountoulaki, Nikos Karacapilidis, Manolis Manatakis
Addresses: Industrial Management and Information Systems Lab, MEAD, University of Patras, 26500 Rion-Patras, Greece. ' Industrial Management and Information Systems Lab, MEAD, University of Patras, 26500 Rion-Patras, Greece. ' Mechanical Engineering and Aeronautics Department, University of Patras, 26500 Rion-Patras, Greece
Abstract: Various multivariate control charts have been proposed for detecting abnormal mean shift changes in a production process to avoid out-of-control signals. Such charts can detect an unusual shift, but they do not directly provide useful information about the shift magnitude or the variable – or group of variables – that has caused the out-of-control signal. The aim of this paper is to improve the control chart interpretation for the case of autocorrelated multivariate process control. In particular, autocorrelated data following the bivariate AR(1) model is used as the reference model. First, a neural-based procedure is used to detect as soon as possible whether an abnormal shift exists and which quality variable has caused the out-of-control signal. Then, four other neural networks (NNs) are trained to estimate shift|s magnitude. Extensive numerically simulated examples are used to evaluate the performance of the proposed methodology.
Keywords: quality control; neural networks; quality management; control charts; mean shift; autocorrelated process control; multivariate process control; statistical process control; SPC.
International Journal of Quality Engineering and Technology, 2011 Vol.2 No.2, pp.114 - 128
Published online: 18 Mar 2011 *Full-text access for editors Access for subscribers Purchase this article Comment on this article