Dynamic multimode process monitoring using recursive GMM and KPCA in a hot rolling mill process

Abstract

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 timevarying multimode faults. The proposed method has also been integrated into an actual system and has been running smoothly in large steel mills in China.

Data-driven anomaly monitoring methods for multimode processes: overview and perspectives

“…文献 [16] 提出了一种改进的动态邻域保持嵌入算法, 能够保持 数据集的局部邻域结构, 对于单一工况的动力学行为, 考虑了序列相关性, 构建一个全局模型, 不需要 先验过程知识. 文献 [10] 利用递归 GMM 方法和递归核 PCA 方法解决工业数据的多工况特性和非平 稳特性, 在 GMM 框架下构建一个全局模型. 文献 [121] 考虑过程的非线性, 利用等价空间方法建立每 个工况的监测模型, 根据贝叶斯融合得到全局结果.…”

“…(1) 闭环反馈下的过程监测. 目前, 大部分的多工况过程异常监测方法都是针对开环系统进行的, 没有考虑闭环反馈控制律下的数据特性变化 [10] . 受闭环控制影响, 工业过程的运行特性和变量间的相 关关系相较于开环系统均会发生变化, 现有的开环系统下异常监测方法无法真正解决实际问题, 需要 重新设计符合实际工业特点的监测方法.…”

Coupling of an analytical rolling model and reinforcement learning to design pass schedules: towards properties controlled hot rolling

“…Optimization procedures are less prevalent, as in reverse rolling the number of passes required is unknown and additionally different objectives with complex interdependencies need to be satisfied. Simultaneously, novel product specifications cause more restrictive process windows (Peng et al, 2021;Schmidtchen & Kawalla, 2016;Shen et al, 2022), leading to even higher demands on the pass schedules design. Novel approaches to finding optimal schedules that improve on established heuristics are being sought here.…”