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Dynamic process monitoring based on a time-serial multi-block modeling approach
Journal of Process Control ( IF 3.316 ) Pub Date : 2020-03-21 , DOI: 10.1016/j.jprocont.2020.03.007
Xinchun Wan; Chudong Tong; Shengjun Meng; Ting Lan

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.
更新日期:2020-03-22

 

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