Premium
Process monitoring for continuous process with periodic characteristics
Author(s) -
Pan Yangdong,
Yoo ChangKyoo,
Lee Jay H.,
Lee InBeum
Publication year - 2004
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.848
Subject(s) - benchmark (surveying) , principal component analysis , kalman filter , process (computing) , computer science , line (geometry) , state space , partial least squares regression , state space representation , data mining , econometrics , statistics , mathematics , algorithm , artificial intelligence , machine learning , geometry , geodesy , geography , operating system
Application of conventional statistical monitoring methods to periodic processes can result in frequent false alarms and/or missed faults due to the non‐stationary behavior seen over a period. To address this problem, we propose to identify and use a stochastic state space model that describes the statistical behavior of changes occurring from period to period. This model, when retooled as a periodically time‐varying model, can be used for on‐line monitoring and estimation with the aid of a Kalman filter. The same model can also be used for inferential estimation of the variables that are difficult or slow to measure on‐line. The proposed approach is applied to a simulation benchmark of a waste water treatment process, which exhibits strong diurnal changes in the feed stream, and is compared against the principal component analysis (PCA) and partial least squares (PLS) methods. Copyright © 2004 John Wiley & Sons, Ltd.