Online monitoring of auto correlated linear profiles via mixed model Online publication date: Tue, 29-Apr-2014
by Paria Soleimani; Ali Narvand; Sadigh Raissi
International Journal of Manufacturing Technology and Management (IJMTM), Vol. 27, No. 4/5/6, 2013
Abstract: In statistical quality control a profile can be characterised by a given mathematical function between a quality characteristic and one or more explanatory process variables. Most existing control charts in the literature have been proposed for profile monitoring with the independence assumption of the observation within profiles. However in certain situation, this assumption can be violated. The present study focused on phase II of a linear profile monitoring and extends Jensen et al. (2008)'s work in applying linear mixed models on the presence of autocorrelation within profiles. Three methods namely Hotteling T², multivariate exponential weighted moving average (MEWMA) control chart and multivariate cumulative sum (MCUSUM) control chart are discussed and their performances are compared in term of average run length (ARL). These techniques are illustrated with a real data set taken from an agriculture field.
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