Abstract
The practitioners are concerned with strip-thickness relevant faults of steel-making cold-rolling continuous annealing process (CAP) which is a typical dynamic nonlinear process. In this paper, a novel data-driven dynamic concurrent kernel canonical correlation analysis (DCKCCA) approach is proposed for the diagnosis of the CAP strip thickness relevant faults. First, a DCKCCA algorithm is proposed to capture dynamic nonlinear correlations between strip thickness and process variables. Strip thickness specific variations, process-specific variations, and thickness-process covariations are monitored respectively. Secondly, a multi-block extension of DCKCCA is designed to compute the contributions according to block partition of lagged variables, in order to help localize faults relevant to abnormal strip thickness. Finally, the proposed methods are illustrated by the application to a real continuous annealing process.
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