Abstract
Most industrial processes are characterized by a system of several variables, all of which are subject to drifts, disturbances, and assignable causes of variation. In the chemical and process industries, there are often inertial forces arising from raw material streams, reactors and tanks that introduce serial correlation over time into these variables. This autocorrelation can have a profound impact on the effectiveness of the statistical monitoring methods used for such processes. This paper reviews some of the available methodology for multivariate process monitoring and shows the effectiveness of principal components in this context. An application of the principal components approach with correlated observation vectors is presented. The effectiveness of this procedure to indicate process upsets is discussed.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 203-210 |
| Number of pages | 8 |
| Journal | Quality and Reliability Engineering International |
| Volume | 12 |
| Issue number | 3 |
| State | Published - 1996 |
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Keywords
- Autocorrelation
- Multivariate quality control
- Principal components analysis
ASJC Scopus subject areas
- Management Science and Operations Research
- Engineering (miscellaneous)
Cite this
Statistical process monitoring with principal components. / Mastrangelo, Christina M.; Runger, George; Montgomery, Douglas.
In: Quality and Reliability Engineering International, Vol. 12, No. 3, 1996, p. 203-210.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Statistical process monitoring with principal components
AU - Mastrangelo, Christina M.
AU - Runger, George
AU - Montgomery, Douglas
PY - 1996
Y1 - 1996
N2 - Most industrial processes are characterized by a system of several variables, all of which are subject to drifts, disturbances, and assignable causes of variation. In the chemical and process industries, there are often inertial forces arising from raw material streams, reactors and tanks that introduce serial correlation over time into these variables. This autocorrelation can have a profound impact on the effectiveness of the statistical monitoring methods used for such processes. This paper reviews some of the available methodology for multivariate process monitoring and shows the effectiveness of principal components in this context. An application of the principal components approach with correlated observation vectors is presented. The effectiveness of this procedure to indicate process upsets is discussed.
AB - Most industrial processes are characterized by a system of several variables, all of which are subject to drifts, disturbances, and assignable causes of variation. In the chemical and process industries, there are often inertial forces arising from raw material streams, reactors and tanks that introduce serial correlation over time into these variables. This autocorrelation can have a profound impact on the effectiveness of the statistical monitoring methods used for such processes. This paper reviews some of the available methodology for multivariate process monitoring and shows the effectiveness of principal components in this context. An application of the principal components approach with correlated observation vectors is presented. The effectiveness of this procedure to indicate process upsets is discussed.
KW - Autocorrelation
KW - Multivariate quality control
KW - Principal components analysis
UR - http://www.scopus.com/inward/record.url?scp=0030147007&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0030147007&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0030147007
VL - 12
SP - 203
EP - 210
JO - Quality and Reliability Engineering International
JF - Quality and Reliability Engineering International
SN - 0748-8017
IS - 3
ER -