Multi-dimension and multi-modal rolling mill vibration prediction model based on multi-level network fusion  

基于多级网络融合的多维度、多模态轧机振动预测模型

作　　者：CHEN Shu-zong LIU Yun-xiao WANG Yun-long QIAN Cheng HUA Chang-chun SUN Jie 陈树宗;刘云;王云龙;钱承;华长春;孙杰(School of Electrical Engineering,Yanshan University,Qinhuangdao 066004,China;Key Lab of Industrial Computer Control Engineering of Hebei Province,Yanshan University,Qinhuangdao 066004,China;Shougang Jingtang United Iron&Steel Co.,Ltd.,Tangshan 063200,China;State Key Laboratory of Rolling and Automation,Northeastern University,Shenyang 110819,China)

机构地区：[1]School of Electrical Engineering,Yanshan University,Qinhuangdao 066004,China [2]Key Lab of Industrial Computer Control Engineering of Hebei Province,Yanshan University,Qinhuangdao 066004,China [3]Shougang Jingtang United Iron&Steel Co.,Ltd.,Tangshan 063200,China [4]State Key Laboratory of Rolling and Automation,Northeastern University,Shenyang 110819,China

出　　处：《Journal of Central South University》2024年第9期3329-3348,共20页中南大学学报（英文版）

基　　金：Project(2023JH26-10100002)supported by the Liaoning Science and Technology Major Project,China;Projects(U21A20117,52074085)supported by the National Natural Science Foundation of China;Project(2022JH2/101300008)supported by the Liaoning Applied Basic Research Program Project,China;Project(22567612H)supported by the Hebei Provincial Key Laboratory Performance Subsidy Project,China。

摘　　要：Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.轧机振动是轧钢生产中的常见问题,其直接影响带钢厚度精度,严重时甚至会导致带钢断裂等事故。现有振动预测模型由于未考虑数据中所包含的周期性特征,导致模型精度有限。针对这些挑战,本文提出了一种基于多层次网络深度融合的多维度、多模态轧机振动时间序列预测模型(MDMMVPM)。该模型综合考虑了多维数据的长期和短期模态特征,并针对不同的数据特征选择了合适的预测算法进行训练,从而提高模型预测精度;基于构建的振动预测模型,分析了轧制参数对轧机振动的影响规律。以某钢厂冷轧机第5机架为研究对象,首次将本文提出的创新模型应用于轧机振动预测。实验结果表明,本文提出的预测模型的预测精度达到92.5%,均方根误差(RMSE)为0.0011,与现有模型相比预测模型的精度显著提高。本文提出的模型同样适用于热轧生产过程,为带钢轧机振动的预测提供了一种新方法和新思路。

关 键 词：rolling mill vibration multi-dimension data multi-modal data convolutional neural network time series prediction 

分 类 号：TG333[金属学及工艺—金属压力加工] TH113.1[机械工程—机械设计及理论]