Model-based process monitoring using robust generalized linear models

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 p...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of production research 2005-04, Vol.43 (7), p.1337-1354
Hauptverfasser: Jearkpaporn, D., Montgomery, D. C., Runger, G. C., Borror, C. M.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207540412331299693