| Summary: | The increasing competitive market has put forward higher demand for iron and steel production process, which is characterized by high-dimensional, nonlinear and multi-scale coupling. The newly rising internet of things (IoT) and advanced communication technologies have promoted the widespread application of data-driven process monitoring methods. To deal with the multimode and non-stationary properties of hot rolling production, a dynamic multimode process monitoring method is proposed based on the recursive Gaussian mixture model (RGMM) and recursive kernel principal component analysis (RKPCA). The proposed approach is applied to the monitoring of the hot-rolled strip thickness oversizing, and comparative experiments are conducted with KPCA, GMM-KPCA on actual production data. Results show that the proposed method shows better performance than conventional methods in terms of fault detection rate and false alarm rate when detecting time-varying multimode faults. The proposed method has also been integrated into an actual system and has been running smoothly in large steel mills in China.
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