基于温度时序特征层次聚类的连铸黏结漏钢预报方法开发

doi:10.3901/JME.2020.08.250

机械工程学报 ›› 2020, Vol. 56 ›› Issue (8): 250-256.doi: 10.3901/JME.2020.08.250

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基于温度时序特征层次聚类的连铸黏结漏钢预报方法开发

段海洋^1,2, 王旭东^1,2, 姚曼^1,2

1. 大连理工大学材料科学与工程学院大连 116024;
2. 大连理工大学辽宁省凝固控制与数字化制备技术重点实验室大连 116024

收稿日期:2019-04-03 修回日期:2019-10-16 出版日期:2020-04-20 发布日期:2020-05-28
通讯作者: 王旭东(通信作者),男,1979年出生,博士,教授,博士研究生导师。主要研究方向为连铸过程模型化及质量控制。E-mail:hler@dlut.edu.cn
作者简介:段海洋,男,1989年出生,博士研究生。主要研究方向为连铸过程异常监测。E-mail:duanhaiyang@mail.dlut.edu.cn
基金资助:
国家自然科学基金（51474047）和中央高校基本科研业务费资助项目。

Development of Sticking Breakout Prediction Method Based on Hierarchical Clustering of Temperature Timing Characteristics in Continuous Casting

DUAN Haiyang^1,2, WANG Xudong^1,2, YAO Man^1,2

1. School of Materials Science and Engineering, Dalian University of Technology, Dalian 116024;
2. Key Laboratory of Solidification Control and Digital Preparation Technology(Liaoning Province), Dalian University of Technology, Dalian 116024

Received:2019-04-03 Revised:2019-10-16 Online:2020-04-20 Published:2020-05-28

摘要/Abstract

摘要： 黏结漏钢的准确识别和预报对于连铸全流程的控制至关重要。黏结漏钢是连铸过程中最具危害的重大事故，如果不能及时、准确地对黏结漏钢进行提前预测和处置，由此带来的漏报、误报不仅严重损坏铸机设备，还将极大影响铸坯质量和生产顺行，带来巨大经济损失。为了捕捉和识别黏结漏钢时结晶器铜板的温度时序特征，将层次聚类（Hierarchical clustering）与动态时间弯曲（Dynamic time warping，DTW）计算方法相结合，构建并开发了一种基于机器学习的新型黏结漏钢预报方法。与现场服役的漏钢预报系统进行测试和对比，结果显示，建立的方法在保证真黏结100%报出率的同时，将误报次数降低了近60%，大幅提高了黏结漏钢预报准确率，避免了由错误报警引起的铸机降速或停机，对于促进连铸过程顺行、稳定和改善连铸坯质量具有积极意义。基于聚类的黏结漏钢预报方法展示出良好的应用潜力，为连铸过程异常监控提供了新思路。

关键词: 时序特征, 层次聚类, 动态时间弯曲, 漏钢预报

Abstract: Accurate identification and prediction for the sticking breakout is of great significance for the control of continuous casting process. Sticking breakout is the most dangerous accident in continuous casting. If the breakout cannot be predicted and disposed in time and accurately, the resulting missing and false alarms will seriously damage the caster equipment and affect the quality of the slabs, which causes huge economic losses. The hierarchical clustering and dynamic time warping (DTW) are combined to capture and identify the temperature timing characteristics during sticking breakout, and then, a new sticking breakout prediction method based on machine learning is developed. Compared with the in-service breakout prediction system, the proposed method can reduce the number of false alarms by about 60% with a 100% correct true alarm rate. The method can greatly improve the prediction accuracy and avoid the casting speed reduction or shutdown caused by false alarms, which is of positive significance for smooth stability and slab quality. The sticking breakout prediction method based on clustering shows excellent application potential and provides a new approach for abnormal monitoring in continuous casting.

Key words: timing characteristics, hierarchical clustering, dynamic time warping, breakout prediction

中图分类号:

TG245

段海洋, 王旭东, 姚曼. 基于温度时序特征层次聚类的连铸黏结漏钢预报方法开发[J]. 机械工程学报, 2020, 56(8): 250-256.

DUAN Haiyang, WANG Xudong, YAO Man. Development of Sticking Breakout Prediction Method Based on Hierarchical Clustering of Temperature Timing Characteristics in Continuous Casting[J]. Journal of Mechanical Engineering, 2020, 56(8): 250-256.

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基于温度时序特征层次聚类的连铸黏结漏钢预报方法开发

Development of Sticking Breakout Prediction Method Based on Hierarchical Clustering of Temperature Timing Characteristics in Continuous Casting

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