基于混合多阶集成模型的非平衡热轧带钢凸度智能诊断

Intelligent diagnosis for hot-rolled strip crown with unbalanced data using a hybrid multi-stage ensemble model

为了提升带钢热轧加工过程的智能化水平,基于数字孪生(DT)和信息物理系统(CPS),文本采用数据驱动方法以诊断热轧带钢凸度。因为热轧工艺具有遗传性、非线性和强耦合性的特点,因此带钢凸度诊断是一个决策边界不明确的非平衡问题。现有回归方法倾向于从多数类样本学习信息,而忽略了少数类的缺陷凸度。为了解决这一问题,本文提出了一个混合多阶集成模型(HMSEN)分类带钢凸度。首先,提出了一个新的采样方法,该方法结合了自适应采样(ADASYN)和重复编辑近邻样本(RENN)以强化对缺陷凸度的关注。随后,基于增加的数据,建立了一个多阶集成模型以提升分类精度。同时,通过分析不同基分类器的组合确定了最佳性能的混合多阶集成模型。实验结果表明,相比于其它采样方法,本文提出的采样方法更适合凸度数据集。此外,混合多阶集成模型的性能要优于现有回归方法和机理模型。因此,对于非平衡热轧带钢凸度智能诊断,本文提出的混合多阶集成模型是一种有效且鲁棒的方法。

To improve the smart manufacturing capabilities of strip hot rolling,based on digital twin(DT)and cyber-physical system(CPS),this paper proposes a data-driven approach for diagnosing hot-rolled strip crown.Since the hot rolling process features heredity,nonlinearity and strong coupling,the diagnosis of strip crown is an imbalanced problem with ill-defined decision boundaries.Conventional regression methods tend to learn more information from the majority class,which ignore the strip with unqualified crown.To address this challenge,a hybrid multi-stage ensemble model(HMSEN)is presented to classify strip crown.Initially,a novel data-resampling method that combines adaptive synthetic sampling(ADASYN)with repeated edited nearest neighbor(RENN)is proposed to assign more attention to unqualified crown.Subsequently,using the reinforced data,a multi-stage ensemble model is built to enhance the classification performance.Furthermore,the best-performing HMSEN is identified by exploring various combinations of base classifiers.The experimental results demonstrated the proposed novel resampling method outperforms comparison methods on crown dataset.Significantly,the proposed HMSEN outperforms not only the existing regression models but also the mechanism model.Therefore,HMSEN is the most robust and effective model to intelligently diagnose hot-rolled strip crown with unbalanced data.