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Detecting abnormal process trends by wavelet‐domain hidden Markov models
Author(s) -
Sun Wei,
Palazoğlu Ahmet,
Romagnoli Jose A.
Publication year - 2003
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.690490113
Subject(s) - wavelet , hidden markov model , pattern recognition (psychology) , artificial intelligence , process (computing) , domain (mathematical analysis) , computer science , variable (mathematics) , markov chain , markov model , mathematics , data mining , machine learning , mathematical analysis , operating system
Abstract A novel method for detection of abnormal conditions during plant operation uses wavelet‐domain hidden Markov models (HMMs) as a powerful tool for statistical modeling of wavelet coefficients. By capturing the interdependence of wavelet coefficients of a measured process variable, a classification strategy is developed that can detect abnormal conditions and classify the process behavior on‐line. The method is extended to include multiple measured variables in detection and classification. Two case studies illustrate the potential of this method.