Model-based process monitoring using robust generalized linear models

D. Jearkpaporn, Douglas Montgomery, George Runger, C. M. Borror

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

Model-based process-monitoring procedures are extremely useful in situations where an output variable of interest is impacted by one or more inputs to the process, and where there are multistage processes with multiple inputs and outputs. To build the model relating input and output variables, the procedure uses historical data, which often contain outliers. To accommodate the presence of these outliers, a robust fitting scheme is introduced for the Generalized Linear Model in process monitoring. Robust deviance residuals are defined and used as the basis of the monitoring procedure. An example and a simulation study for a gamma-distributed response are included. The average run length performance reveals that the procedure is effective for detecting small process shifts when outliers are present.

Original languageEnglish (US)
Pages (from-to)1337-1354
Number of pages18
JournalInternational Journal of Production Research
Volume43
Issue number7
DOIs
StatePublished - Apr 1 2005

Fingerprint

Process monitoring
Monitoring
Generalized linear model
Outliers

Keywords

  • Gamma-distributed data
  • M-estimator
  • Robust deviance
  • Robust generalized linear models (GLMs)

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Management Science and Operations Research

Cite this

Model-based process monitoring using robust generalized linear models. / Jearkpaporn, D.; Montgomery, Douglas; Runger, George; Borror, C. M.

In: International Journal of Production Research, Vol. 43, No. 7, 01.04.2005, p. 1337-1354.

Research output: Contribution to journalArticle

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