Abstract
A time-serial multi-block modeling (TSMBM) algorithm is proposed for dynamic process monitoring, which considers a unified framework of multi-block modeling and auto-correlation extraction. The proposed TSMBM-based method first constructs time-serial multi-blocks according to the sampling time nodes, the correlation between different blocks then serves as a good representation for the auto-correlated characteristic in the given data. With the utilization of multi-block projecting bases, three categories of auto-correlated variations can be captured in different block models. Furthermore, the Kalman filter is employed to generate dynamic noise and measurement noise inheriting little auto-correlation for online monitoring purposes. Finally, the effectiveness and superiority of the proposed method are validated through comparisons with other state-of-art dynamic process monitoring approaches.
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