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Statistical signatures used with principal component analysis for fault detection and isolation in a continuous reactor
Journal of chemometrics, 2006-01, Vol.20 (1-2), p.34-42
Miller, John P.
2006
Volltextzugriff (PDF)
Details
Autor(en) / Beteiligte
Miller, John P.
Titel
Statistical signatures used with principal component analysis for fault detection and isolation in a continuous reactor
Ist Teil von
Journal of chemometrics, 2006-01, Vol.20 (1-2), p.34-42
Ort / Verlag
Chichester, UK: John Wiley & Sons, Ltd
Erscheinungsjahr
2006
Quelle
Wiley Online Library All Journals
Beschreibungen/Notizen
Principal component analysis (PCA) is a technique widely used in industrial process control for data analysis and reduction. A score discriminant can be used in conjunction with a PCA model to differentiate between the normal operating condition and an abnormal condition. To illustrate application of these analytical techniques, raw data is collected from a high fidelity simulation of a continuous chemical reactor for both the normal operating condition, and several different fault conditions. A PCA model and score discriminant are applied to analyze the raw data, but this approach does not reliably differentiate between all process conditions. To improve the differentiation, an alternative model is developed using the statistical signatures of mean and standard deviation, such as are computed in some present day intelligent field devices. The new PCA score discriminant model based on statistical signatures produces a much clearer differentiation between all process conditions. Copyright © 2006 John Wiley & Sons, Ltd.
Sprache
Englisch
Identifikatoren
ISSN: 0886-9383
eISSN: 1099-128X
DOI: 10.1002/cem.979
Titel-ID: cdi_crossref_primary_10_1002_cem_979
Format
–
Schlagworte
Chemistry
,
Data analysis
,
Discriminant analysis
,
Exact sciences and technology
,
fault detection and isolation
,
Fault diagnosis
,
General and physical chemistry
,
principal component analysis
,
Principal components analysis
,
reactor
,
Signatures
,
Statistical process control
,
statistics
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