Model-based control chart for autoregressive and correlated data

Elvira N. Loredo, Duangporn Jearkpaporn, Connie M. Borror

Research output: Contribution to journalArticle

39 Citations (Scopus)

Abstract

In recent years, statistical process control for autocorrelated processes has received a great deal of attention. This is due in part to the improvements in measurement and data collection that allow processes to be sampled at higher frequency rates and, hence, data autocorrelation. A method for monitoring autocorrelated processes based on regression adjustment is presented in this paper. The performance of the residual-based control chart in terms of the average run length is compared to observation-based control charts via Monte Carlo simulations. In general, the observation-based control charts perform very poorly when data are correlated over time. Under the assumption that the model is correct, the residual-based control charts are superior for all cases considered here. This suggests using a residual-based control chart to detect the mean shift. This is recommended particularly for chemical processes where there are often cascade processes with several inputs but only a few outputs, and where many of the variables are highly autocorrelated.

Original languageEnglish (US)
Pages (from-to)489-496
Number of pages8
JournalQuality and Reliability Engineering International
Volume18
Issue number6
DOIs
StatePublished - Nov 2002

Fingerprint

Statistical process control
Process monitoring
Autocorrelation
Control charts
Monte Carlo simulation
Data collection
Average run length
Mean shift
Cascade

Keywords

  • Autocorrelated processes
  • Cascade processes
  • Model-based control chart
  • Residual-based control chart

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Engineering (miscellaneous)

Cite this

Model-based control chart for autoregressive and correlated data. / Loredo, Elvira N.; Jearkpaporn, Duangporn; Borror, Connie M.

In: Quality and Reliability Engineering International, Vol. 18, No. 6, 11.2002, p. 489-496.

Research output: Contribution to journalArticle

Loredo, Elvira N. ; Jearkpaporn, Duangporn ; Borror, Connie M. / Model-based control chart for autoregressive and correlated data. In: Quality and Reliability Engineering International. 2002 ; Vol. 18, No. 6. pp. 489-496.
@article{5f6de1c16e6b45438634d598d47ccc7c,
title = "Model-based control chart for autoregressive and correlated data",
abstract = "In recent years, statistical process control for autocorrelated processes has received a great deal of attention. This is due in part to the improvements in measurement and data collection that allow processes to be sampled at higher frequency rates and, hence, data autocorrelation. A method for monitoring autocorrelated processes based on regression adjustment is presented in this paper. The performance of the residual-based control chart in terms of the average run length is compared to observation-based control charts via Monte Carlo simulations. In general, the observation-based control charts perform very poorly when data are correlated over time. Under the assumption that the model is correct, the residual-based control charts are superior for all cases considered here. This suggests using a residual-based control chart to detect the mean shift. This is recommended particularly for chemical processes where there are often cascade processes with several inputs but only a few outputs, and where many of the variables are highly autocorrelated.",
keywords = "Autocorrelated processes, Cascade processes, Model-based control chart, Residual-based control chart",
author = "Loredo, {Elvira N.} and Duangporn Jearkpaporn and Borror, {Connie M.}",
year = "2002",
month = "11",
doi = "10.1002/qre.497",
language = "English (US)",
volume = "18",
pages = "489--496",
journal = "Quality and Reliability Engineering International",
issn = "0748-8017",
publisher = "John Wiley and Sons Ltd",
number = "6",

}

TY - JOUR

T1 - Model-based control chart for autoregressive and correlated data

AU - Loredo, Elvira N.

AU - Jearkpaporn, Duangporn

AU - Borror, Connie M.

PY - 2002/11

Y1 - 2002/11

N2 - In recent years, statistical process control for autocorrelated processes has received a great deal of attention. This is due in part to the improvements in measurement and data collection that allow processes to be sampled at higher frequency rates and, hence, data autocorrelation. A method for monitoring autocorrelated processes based on regression adjustment is presented in this paper. The performance of the residual-based control chart in terms of the average run length is compared to observation-based control charts via Monte Carlo simulations. In general, the observation-based control charts perform very poorly when data are correlated over time. Under the assumption that the model is correct, the residual-based control charts are superior for all cases considered here. This suggests using a residual-based control chart to detect the mean shift. This is recommended particularly for chemical processes where there are often cascade processes with several inputs but only a few outputs, and where many of the variables are highly autocorrelated.

AB - In recent years, statistical process control for autocorrelated processes has received a great deal of attention. This is due in part to the improvements in measurement and data collection that allow processes to be sampled at higher frequency rates and, hence, data autocorrelation. A method for monitoring autocorrelated processes based on regression adjustment is presented in this paper. The performance of the residual-based control chart in terms of the average run length is compared to observation-based control charts via Monte Carlo simulations. In general, the observation-based control charts perform very poorly when data are correlated over time. Under the assumption that the model is correct, the residual-based control charts are superior for all cases considered here. This suggests using a residual-based control chart to detect the mean shift. This is recommended particularly for chemical processes where there are often cascade processes with several inputs but only a few outputs, and where many of the variables are highly autocorrelated.

KW - Autocorrelated processes

KW - Cascade processes

KW - Model-based control chart

KW - Residual-based control chart

UR - http://www.scopus.com/inward/record.url?scp=0036864066&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0036864066&partnerID=8YFLogxK

U2 - 10.1002/qre.497

DO - 10.1002/qre.497

M3 - Article

VL - 18

SP - 489

EP - 496

JO - Quality and Reliability Engineering International

JF - Quality and Reliability Engineering International

SN - 0748-8017

IS - 6

ER -