Process monitoring for continuous process with periodic characteristics

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.