Online monitoring of auto correlated linear profiles via mixed model

Paria Soleimani, Ali Narvand and Sadigh Raissi

International Journal of Manufacturing Technology and Management, 2013, vol. 27, issue 4/5/6, 238-250

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.

Keywords: multivariate process monitoring; linear profile monitoring; mixed models; modelling; multivariate cumulative sum; MCUSUM; multivariate exponential weighted moving average; MEWMA; HottelingT²; auto correlation; average run length; statistical quality control; SQC; control charts; agriculture. (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (2)

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