摘要
冷连轧生产过程中,由于轧机振动异常可能造成产品质量问题甚至发生断带等影响正常生产,极大限制了生产效率。目前已有钢铁企业实现在线颤振监测,但监测系统只能通过振动报警后再进行降速等方式来抑制振动。针对冷连轧振动难以建立精确的传统机理模型,利用现场实测生产数据,建立梯度提升决策树模型进行振动能量回归,并利用梯度提升决策树算法特性进行特征选择以寻找影响振动的重要因素并进行模型简化。实际生产数据仿真结果表明,通过梯度提升决策树所建模型能够有效选取重要因素、降低模型复杂度,而且建立的回归模型能够准确反映轧制振动能量的变化趋势。
In production process of tandem cold rolling,abnormal vibration of rolling mill may cause product quality problems and even strip breakage to greatly limit production efficiency.At present,online flutter monitoring is realized in iron and steel enterprises,but the monitoring system can only suppress vibration through vibration alarm and then speed down.Here,aiming at the difficulty to establish accurate traditional mechanism model for cold rolling vibration,using production data measured on site,the gradient boosted decision tree model was established to perform vibration energy regression.Characteristics of the gradient boosted decision tree algorithm were used to do feature selection,find important factors affecting vibration and simplify the model.The simulation results of actual production data showed that the model established with the gradient boosted decision tree can effectively select important factors,and reduce its complexity;the established regression model can accurately reflect the varying trend of rolling vibration energy.
作者
周晓敏
郝勇凯
丛文韬
魏志彬
温国栋
ZHOU Xiaomin;HAO Yongkai;CONG Wentao;WEI Zhibin;WEN Guodong(School of Mechanical Engineering,University of Science and Technology Beijing,Beijing 100083,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2021年第13期154-158,共5页
Journal of Vibration and Shock
基金
国家自然科学基金(51775038)。
关键词
梯度提升决策树
特征选择
冷轧颤振
gradient boosted decision tree
feature selection
flutter of cold rolling
作者简介
第一作者:周晓敏,女,副教授,1975年生;通信作者:郝勇凯,男,硕士生,1995年生。
