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中南大学学报(自然科学版)

Journal of Central South University

第49卷    第3期    总第283期    2018年3月

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文章编号:1672-7207(2018)03-0574-09
基于批次回归系数的热轧带钢头部拉窄过程监控与诊断
孙勇1, 2,何飞1,杨德斌2

(1. 北京科技大学 钢铁共性技术协同创新中心,北京,100083; 2. 北京科技大学 机械工程学院,北京,100083)

摘 要: 针对目前热轧带钢生产过程数据分析主要利用工艺参数和产品质量的平均值,忽略带钢长度方向上的变异信息,提出一种新的三维生产过程数据的监控框架。首先建立热连轧机组精轧过程中每块带钢的工艺变量与宽度间的偏最小二乘模型,获得所有批次的回归系数并组成二维数据矩阵,利用回归系数矩阵建立主成分分析模型进行监控和诊断。研究结果表明:该方法可以有效获取过程变量对质量的影响关系、实现过程监控,并有效给出质量异常的原因。

 

关键词: 批次数据分析;偏最小二乘回归;回归系数;过程监控;质量诊断

Process monitoring and diagnosis of head width narrowing of hot rolled strip based on regression coefficients of different batches
SUN Yong1, 2, HE Fei1, YANG Debin2

1. Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China; 2. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China

Abstract:Considering that the current data analysis methods usually use the mean value of process and quality parameters in hot rolled strip production without including the variation information in the longitudinal direction of the strip, a novel framework was introduced for process monitoring using three-way dataset. Firstly, the partial least squares model between process variables and width of each strip after finishing hot rolling was built. And then, a two-way matrix was obtained, which consisted of regression coefficients of all batches. Finally, regression coefficients matrix was used to process monitoring and diagnosis based on principal component analysis. The results show that the new method can not only effectively obtain the relationship between the process and quality parameter, but also finish process monitoring and explain why there appears abnormal quality.

 

Key words: batch data analysis; partial least squares regression; regression coefficients; process monitoring; quality diagnosis

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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