基于密度聚类和动态时间弯曲的结晶器黏结漏钢预报方法的开发  

Development of prediction method for mold sticking breakout based on density-based spatial clustering of applications with noise and dynamic time warping

作　　者：段海洋 王旭东[1,2] 姚曼 DUAN Hai-yang;WANG Xu-dong;YAO Man(School of Materials Science and Engineering,Dalian University of Technology,Dalian 116024,China;Key Laboratory of Solidification Control and Digital Preparation Technology(Liaoning Province),Dalian 116024,China)

机构地区：[1]大连理工大学材料科学与工程学院,大连116024 [2]辽宁省凝固控制与数字化制备技术重点实验室,大连116024

出　　处：《工程科学学报》2020年第3期348-357,共10页Chinese Journal of Engineering

基　　金：国家自然科学基金资助项目(51974056,51474047);中央高校基本科研业务费资助项目。

摘　　要：针对漏钢时结晶器铜板温度呈现出的"时间滞后"和"空间倒置"等典型特征,本文通过引入动态时间弯曲(DTW)和机器学习中的密度聚类(DBSCAN)方法,提取、汇集并区分结晶器温度的典型变化模式,在此基础上开发出一种新型的漏钢预报方法.借助动态时间弯曲度量不同拉速、钢种或工艺操作条件下结晶器热电偶温度的相似性,并运用密度聚类方法聚集和分离正常工况、黏结漏钢状况下的温度样本,在此基础上检测和预报结晶器漏钢.结果证实,相较于传统的逻辑判断和人工神经元网络预报结晶器漏钢的方法,基于聚类的漏钢预报方法无需人为设置阈值或参数,能够依据漏钢历史样本中温度变化的共性规律,提取并融合热电偶温度在时间、空间上典型的变化特征,准确区分和预报结晶器漏钢,具有较好的自适应性和鲁棒性.As the core component of continuous casting machines, complex behaviors of fluid flow, heat transfer, mass transfer, and solidification occurring inside the mold are the key factors affecting the slabs quality. Breakout is one of the most catastrophic accidents in continuous casting process, which brings severe impacts on personal security, smooth producing, slab quality, and caster equipment.In particular, with the development of the high-speed casting technology, quality defects and sticking breakouts caused by high-load emerge frequently and missing or false alarms for online prediction of breakout occasionally occur. Thus, accurate identification and prediction for the mold breakout is a top priority for online processing control. Considering the typical temperature characteristics of"time lag" and "space inversion" during a breakout, this paper introduced the concepts of dynamic time warping(DTW) and densitybased spatial clustering of applications with noise(DBSCAN) in machine learning. On the basis of collecting and distinguishing the typical change modes of mold temperature, an integrated novel method for predicting breakout was developed. The proposed method applied DTW to measure the similarity of mold thermocouple temperature under different casting speeds, steel grades, and other operating conditions, while DBSCAN was used to cluster and separate the temperature samples between normal casting status and sticking breakout.On the basis of the above mentioned method,the results show that the mold sticking breakout can be effectively detected and predicted.Compared with the traditional method based on logical judgment and artificial neural network,the clusteringbased breakout prediction method does not require manual setting of thresholds or parameters.According to the common rule of temperature variation in historical samples of breakout,the typical characteristics of temperature in time and space can be extracted and fused,and the breakout can be accurately distinguished and predicted,which shows good se

关 键 词：动态时间弯曲 人工神经元网络 密度聚类 热电偶温度 工艺操作条件 时间滞后 共性规律 机器学习 

分 类 号：TG249.7[金属学及工艺—铸造]