基于自编码器的冷轧带材板形数据降维方法  

Dimension reduction method of cold rolling strip flatness data based on autoencoder

作　　者：徐扬欢 王东城 汪永梅[1] 袁文越 于华鑫 刘宏民 XU Yang-huan;WANG Dong-cheng;WANG Yong-mei;YUAN Wen-yue;YU Hua-xin;LIU Hong-min(National Engineering Research Center for Equipment and Technology of Cold Rolling Strip,Yanshan University,Qinhuangdao 066004,Hebei,China;State Key Laboratory of Metastable Materials Science and Technology,Yanshan University,Qinhuangdao 066004,Hebei,China;Northeastern University at Qinhuangdao,Qinhuangdao 066004.Hebei,China)

机构地区：[1]燕山大学国家冷轧板带装备及工艺工程技术研究中心,河北秦皇岛066004 [2]燕山大学亚稳材料制备技术与科学国家重点实验室,河北秦皇岛066004 [3]东北大学秦皇岛分校,河北秦皇岛066004

出　　处：《钢铁》2021年第9期26-35,共10页Iron and Steel

基　　金：河北省高端人才和“巨人计划”创新团队资助项目(2019)。

摘　　要：为实现板带轧制过程的智能制造,对智能化的内涵进行了深入探索。针对具体问题,将无监督学习与强化学习理论用于生产实践具有重要意义。以板带轧制过程中的板形检测数据为研究对象,通过无监督学习理论中的自编码器进行板形基本模式的自动学习,从而降低板形数据的存储与传输量,实现板形分布的抽象表示,为后续板形异常检测、智能预报和智能控制奠定基础。与基于勒让德多项式模式的传统板形数据降维方法相比,此方法可显著提高板形重构精度,实现板形数据的近似无损压缩。In order to realize the intelligent manufacturing of plate and strip rolling process,it is necessary to deeply explore the connotation of intelligent manufacturing.For specific problems,it is of great significance to apply the unsupervised learning and the reinforcement learning theory to production practice.The flatness detection data in the process of strip rolling is taken as the research object and the autoencoder in the unsupervised learning theory is used to automatically learn the basic flatness mode,so as to reduce the amount of storage and transmission of flatness data,realize the abstract representation of flatness distribution,and lay the foundation for the flatness anomaly detection,the intelligent prediction and the intelligent control.Compared with the traditional flatness data dimension reduction method based on Legendre polynomial,the accuracy of flatness reconstruction can be significantly improved and the approximate lossless compression of the flatness data can be realized applying the present method.

关 键 词：冷轧带材 板形 自编码器 降维 无监督学习 

分 类 号：TG335.56[金属学及工艺—金属压力加工] TP18[自动化与计算机技术—控制理论与控制工程]