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Improvements in the Development of Statistical Models for Process Monitoring
& Detection
Authors:
Karlene A.
Hoo1 and M. J. Piovoso2
1Department of
Chemical Engineering, University of South Carolina, Columbia, SC
2Graduate
School, Penn State University, Malvern, PA
Abstract
Producing
a uniform product is important for several reasons such as maintaining a
competitive position, reducing the number of shutdowns and startups,
and eliminating of the sources of variability. Multivariate statistical
methods caQ assist in the identification of process correlations and
the development of process monitoring models [1, 2]. This work extends
these concepts by demonstrating that the correlations and resulting
monitoring models can be improved greatly with the addition of prefiltering
the time signals using a median filter and time-scale decomposition
using a multiresolution wavelet function. After the data are filtered
and decomposed, the multivariate statistical method of principal
component analysis (PCA) is used to develop a process monitoring model.
Data taken from a difficult to operate industrial process are used to
demonstrate these ideas.Publication: Presented at ASA Conference,
Chicago, IL, 1997.
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