An algorithm for quality control charts for autocorrelated data

Author

Camargo, M.E. ; Filho, W.P. ; Russo, S.L. ; Dullius, A.I.S. ; Motta, M.E.V. ; Dorion, E.

Author_Institution

Post-Grad. Program in Adm., Univ. of Caxias do Sul, Caxias do Sul, Brazil

fYear

2010

fDate

2-5 June 2010

Firstpage

297

Lastpage

299

Abstract

Currently considerable attention has been given to the effect of data correlation on statistical process control (SPC). Use of traditional SPC methods when observations are correlated often leads to misleading conclusions as to whether or not the process is under control. The objective of this paper is to develop an algorithm to adjust a model ARMA(p,q), for calculate the run length distribution (RLD), the average run length (ARL), and the standard deviation of the run length (SRL), for residual control charts X(ind) and MR used to monitor autocorrelated processes. The algorithm was used for analysis of real data. We conclude that for negative first-order autocorrelation, the residuals chart is performing better than the Shewhart chart for independent observations.

Keywords

autoregressive moving average processes; quality control; statistical analysis; Shewhart chart; autocorrelated data; autocorrelated processes; average run length; first-order autocorrelation; model ARMA; quality control charts; residual control charts; run length distribution; standard deviation run length; statistical process control; Autocorrelation; Control charts; Data analysis; Distributed computing; Electronic mail; Monitoring; Parameter estimation; Polynomials; Process control; Quality control; Algorithm; Autocorrelated Data; Residual Control Charts;

fLanguage

English

Publisher

ieee

Conference_Titel

Management of Innovation and Technology (ICMIT), 2010 IEEE International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4244-6565-1

Electronic_ISBN

978-1-4244-6566-8

Type

conf

DOI

10.1109/ICMIT.2010.5492705

Filename

5492705