Process monitoring and diagnosis of head width narrowing of hot rolled strip based on regression coefficients of different batches

被引：0
作者：
Sun Y. [1 ,2 ]
He F. [1 ]
Yang D. [2 ]
机构：
[1] Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing
[2] School of Mechanical Engineering, University of Science and Technology Beijing, Beijing
来源：
He, Fei (hefei@ustb.edu.com) | 2018年 / Central South University of Technology卷 / 49期
基金：
中国国家自然科学基金;
关键词：
Batch data analysis; Partial least squares regression; Process monitoring; Quality diagnosis; Regression coefficients;
D O I：
10.11817/j.issn.1672-7207.2018.03.009
中图分类号：
学科分类号：
摘要：
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. © 2018, Central South University Press. All right reserved.
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页码：574 / 582
页数：8
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