摘要
针对轧制过程实际数据噪声大、难以获取准确板形调控功效系数的问题,提出了一种融合集成经验模态分解(EEMD)和小波变换(WT)的数据降噪方法。将含有噪声的实际生产数据经过EEMD分解后,利用小波变换方法对噪声主导的本征模态分量(IMF)进行降噪处理,处理后的噪声分量与其余分量重构得到降噪后数据,并结合结构方程模型(SEM)计算得到板形功效系数。利用1450 mm五机架冷连轧生产线实际数据进行试验,结果表明,EEMD-WT-SEM方法可以有效降低数据噪声,有效提升板形调控功效系数的准确性。
Aiming at the problem that it is difficult to obtain accurate flatness control efficiency coefficients because of the large noise in the actual data of the rolling process,a data noise reduction method combining ensemble empirical modal decomposition(EEMD)and wavelet transform(WT)is presented.Firstly,EEMD is used to decompose the original data and the intrinsic mode function(IMF)divided into two parts,the noise dominant components and the signal dominant components.Then,the wavelet transform denoising method is used to conduct noise reduction of the noise dominant components.Finally,the flatness actuator efficiency coefficients are calculated from the reconstructed data by the structural equation model(SEM)method.The actual data of a 1450 mm five-stand cold tandem rolling production line was used for testing,the results show that the EEMD-WT-SEM method can effectively reduce the data noise and improve the accuracy of the efficiency coefficient.
作者
孙杰
单鹏飞
彭文
张殿华
SUN Jie;SHAN Peng-fei;PENG Wen;ZHANG Dian-hua(The State Key Laboratory of Rolling and Automation,Northeastern University,Shenyang 110819,Liaoning,China)
出处
《钢铁》
CAS
CSCD
北大核心
2021年第6期67-74,119,共9页
Iron and Steel
基金
国家重点研发计划资助项目(2018YFB1308700)
国家自然科学基金资助项目(51774084,51634002)
中央高校基本科研业务费专项资金资助项目(N170708020,N2004010)
辽宁省“兴辽英才计划”资助项目(XLYC1907065)。
关键词
冷轧
数据降噪
集成经验模态分解
板形调控功效
板形控制
cold rolling
data noise reduction
ensemble empirical modal decomposition
flatness actuator efficiency
flatness control
