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Statistical signatures used with principal component analysis for fault detection and isolation in a continuous reactor
Author(s) -
Miller John P.
Publication year - 2006
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.979
Subject(s) - principal component analysis , linear discriminant analysis , pattern recognition (psychology) , fault detection and isolation , computer science , discriminant , dimensionality reduction , artificial intelligence , process (computing) , statistical process control , standard deviation , data mining , statistics , mathematics , actuator , operating system
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.