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 language | English (US) |
|---|---|
| Pages (from-to) | 1337-1354 |
| Number of pages | 18 |
| Journal | International Journal of Production Research |
| Volume | 43 |
| Issue number | 7 |
| DOIs | |
| State | Published - Apr 1 2005 |
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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 journal › Article
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TY - JOUR
T1 - Model-based process monitoring using robust generalized linear models
AU - Jearkpaporn, D.
AU - Montgomery, Douglas
AU - Runger, George
AU - Borror, C. M.
PY - 2005/4/1
Y1 - 2005/4/1
N2 - 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.
AB - 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.
KW - Gamma-distributed data
KW - M-estimator
KW - Robust deviance
KW - Robust generalized linear models (GLMs)
UR - http://www.scopus.com/inward/record.url?scp=27744486306&partnerID=8YFLogxK
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U2 - 10.1080/00207540412331299693
DO - 10.1080/00207540412331299693
M3 - Article
AN - SCOPUS:27744486306
VL - 43
SP - 1337
EP - 1354
JO - International Journal of Production Research
JF - International Journal of Production Research
SN - 0020-7543
IS - 7
ER -