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

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

作者机构：School of Electrical EngineeringYanshan UniversityQinhuangdao 066004China Key Lab of Industrial Computer Control Engineering of Hebei ProvinceYanshan UniversityQinhuangdao 066004China Shougang Jingtang United Iron&Steel Co.Ltd.Tangshan 063200China State Key Laboratory of Rolling and AutomationNortheastern UniversityShenyang 110819China 

出 版 物：《Journal of Central South University》 (中南大学学报（英文版）)

年 卷 期：2024年第31卷第9期

页      面：3329-3348页

核心收录：

学科分类：080503[工学-材料加工工程] 08[工学] 080203[工学-机械设计及理论] 0805[工学-材料科学与工程（可授工学、理学学位）] 0802[工学-机械工程] 080201[工学-机械制造及其自动化] 

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 

主　　题：rolling mill vibration multi-dimension data multi-modal data convolutional neural network time series prediction 

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 *** existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model *** 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 *** 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 *** on the established prediction model,the effects of tension and rolling force on mill vibration are *** 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 *** 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 *** proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.