李天伦, 何安瑞, 邵健, 付文鹏, 强毅, 谢向群. 基于Copula函数的热轧支持辊健康状态预测模型[J]. 工程科学学报, 2020, 42(6): 787-795. DOI: 10.13374/j.issn2095-9389.2019.08.26.001
引用本文: 李天伦, 何安瑞, 邵健, 付文鹏, 强毅, 谢向群. 基于Copula函数的热轧支持辊健康状态预测模型[J]. 工程科学学报, 2020, 42(6): 787-795. DOI: 10.13374/j.issn2095-9389.2019.08.26.001
LI Tian-lun, HE An-rui, SHAO Jian, FU Wen-peng, QIANG Yi, XIE Xiang-qun. Copula-based model for hot-rolling back-up roll health prediction[J]. Chinese Journal of Engineering, 2020, 42(6): 787-795. DOI: 10.13374/j.issn2095-9389.2019.08.26.001
Citation: LI Tian-lun, HE An-rui, SHAO Jian, FU Wen-peng, QIANG Yi, XIE Xiang-qun. Copula-based model for hot-rolling back-up roll health prediction[J]. Chinese Journal of Engineering, 2020, 42(6): 787-795. DOI: 10.13374/j.issn2095-9389.2019.08.26.001

基于Copula函数的热轧支持辊健康状态预测模型

Copula-based model for hot-rolling back-up roll health prediction

  • 摘要: 热轧支持辊的健康状态在带钢板形质量和轧制稳定性控制中起着关键作用,非线性、强耦合、少样本等特点使得热轧支持辊健康状态的预测复杂,目前各大钢厂仍以定期维护和事后维修为主。本文提出了一种支持辊虚拟健康指数的构建方法以及基于Copula函数的复杂工况健康状态预测模型。首先结合支持辊弯窜辊数据表征支持辊健康状态,再使用K-means聚类方法对支持辊工况进行划分,将各工况下过程数据分别构建Copula预测模型,最后根据实际轧制计划的排布顺序融合各工况模型的预测结果。提出的基于Copula函数的预测模型在某钢厂1780热连轧产线得到应用,结果表明,该模型能够准确有效的按照轧制计划实现支持辊的健康状态预测,以更科学的策略指导支持辊更换维护。

     

    Abstract: The health condition of hot-rolling back-up rolls plays a key role in controlling the strip profile quality and rolling stability. The characteristics of nonlinearity, strong coupling, and the use of limited samples complicate the prediction of the back-up roll health state. The current back-up roll replacement strategy of each steel mill is generally determined according to a certain rolling time or rolling kilometer, and such a maintenance mode is based on experience. In actual experience, due to different strip specifications in each rolling cycle, the degrees of wear on the back-up rolls are different. Regular maintenance methods may easily lead to excessive wear of the back-up rolls and reduce the quality of the strip shape at the end of the unit, or premature roll replacement wastes the back-up roll performance. This paper proposed a construction method for the back-up roll virtual health index and a Copula function–based model for predicting the health condition of complex working conditions. The health condition of a pair of back-up rolls was characterized by combining roll bending and shifting data, and the back-up roll condition was divided by the K-means clustering method. The Copula prediction model was constructed using the process data under each working condition, and finally, according to the actual rolling schedule, the arrangement order combines the prediction results of the working conditions. The production performance data of a 1780-mm hot rolling line were used to verify the results. The results show that the proposed Copula-based prediction model can accurately and effectively predict the health condition of the back-up roll according to the rolling schedule; thus, it can serve as the basis of a more scientific strategy to guide the replacement and maintenance of the back-up roll.

     

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