Abstract
This paper presents the study on the data-driven process monitoring system design for the dynamic processes with deterministic disturbance. The basic idea of the proposed method is to identify the stable kernel representation (SKR) of the dynamic process by decomposing the process data into different subspaces. By extracting the maximum influence of the disturbance from fault-free data, a process monitoring system is developed based on the identified data-driven SKR. The performance and effectiveness of the proposed scheme are verified and demonstrated through the numerical study on randomly generated systems.
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