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Wavelet‐based density estimation and application to process monitoring
Author(s) -
Safavi A. A.,
Chen J.,
Romagnoli J. A.
Publication year - 1997
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690430512
Subject(s) - multivariate kernel density estimation , wavelet , estimator , density estimation , process (computing) , multiresolution analysis , multivariate statistics , computer science , wavelet transform , estimation , artificial intelligence , mathematics , statistics , pattern recognition (psychology) , algorithm , discrete wavelet transform , engineering , kernel method , variable kernel density estimation , support vector machine , operating system , systems engineering
Abstract An application of wavelets and multiresolution analysis to density estimation and process monitoring is presented. Wavelet‐based density‐estimation techniques are developed as an alternative and superior method to other common density‐estimation techniques. Also shown is the effectiveness of wavelet estimators when the observations are dependent. The resulting density estimators are then used in defining a normal operating region for the process under study so that any abnormal behavior by the process can be monitored. Results of applying these techniques to a typical multivariate chemical process are also presented.