Detecting abnormal process trends by wavelet-domain hidden Markov models

被引：24
|
作者：
Sun, W
Palazoglu, A [1 ]
Romagnoli, JA
机构：
[1] Univ Calif Davis, Dept Chem Engn & Mat Sci, Davis, CA 95616 USA
[2] Univ Sydney, Dept Chem Engn, Sydney, NSW 2006, Australia
来源：
AICHE JOURNAL | 2003年 / 49卷 / 01期
关键词：
D O I：
10.1002/aic.690490113
中图分类号：
TQ [化学工业];
学科分类号：
0817 ;
摘要：
A novel method for detection of abnormal conditions during plant operation uses wauelet-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.
引用
收藏
页码：140 / 150
页数：11
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