Abstract：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 1 450 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.
[1] 李伯群,范璇,李伟红,等. 板形设定模型参数优化与控制技术应用[J]. 钢铁,2019,54(2):41.(LI Bo-qun,FAN Xuan,LI Wei-hong,et al. Parameter optimization of shape setting model and application of control technology[J]. Iron and Steel,2019,54(2):41.) [2] WANG D C,LIU H M,LIU J. Research and development trend of shape control for cold rolling strip[J]. Chinese Journal of Mechanical Engineering,2017,30(5):1248. [3] 黄长清,赵旻. 冷轧板带板形识别及控制的研究进展[J]. 钢铁研究学报,2013,25(12):1. (HUANG Chang-qing,ZHAO Min. Research progress of plate-profile recognition and control for cold-rolled strip[J]. Journal of Iron and Steel Research,2013,25(12):1.) [4] 贾大朋,李韶岗,米楠,等. 1 250 mm五机架六辊冷连轧工艺控制系统核心技术[J]. 轧钢,2020,37(3):71.(JIA Da-peng,LI Shao-gang,MI Nan,et al. Core technology of process control system for 1 250 mm five stands 6-high tandem cold mill[J]. Steel Rolling,2020,37(3):71.) [5] 赵忠云, 张金旺, 黄斌. 冷轧基板起筋成因分析及控制实践[J]. 中国冶金,2019,29(9):73.(ZHAO Zhong-yun,ZHANG Jin-wang,HUANG Bin. Cause analysis and control practice of ridge buckle of cold rolling substrate[J]. China Metallurgy,2019,29(9):73.) [6] 王少飞,孙光中,窦爱民,等. 热轧原料对冷连轧机板形控制的影响[J]. 轧钢,2019,36(2):35.(WANG Shao-fei,SUN Guang-zhong,DOU Ai-min,et al. Influence of hot rolled strip on shape control of cold tandem mill[J]. Steel Rolling,2019,36(2):35.) [7] 沈新玉,胡柯,李宏洲,等. UCM冷连轧机边降控制工作辊辊形优化[J]. 中国冶金,2019,29(11):55.(SHEN Xin-yu,HU Ke,LI Hong-zhou,et al. Work roll contour optimization for edge drop control based on UCM cold rolling mills[J]. China Metallurgy,2019,29(11):55.) [8] 王新东,闫永军. 智能制造助力钢铁行业技术进步[J]. 冶金自动化,2019,43(1):1.(WANG Xin-dong,YAN Yong-jun. Promoting the technical progress of steel industry with intelligent manufacturing[J]. Metallurgical Industry Automation,2019,43(1):1.) [9] 曹建国,江军,赵秋芳,等. 基于数据挖掘的宽厚板板凸度控制[J]. 中南大学学报(自然科学版),2019,50(11):2743.(CAO Jian-guo,JIANG Jun,ZHAO Qiu-fang. Wide and heavy plate crown control based on data mining[J]. Journal of Central South University(Science and Technology),2019,50(11):2743.) [10] 宋蕾,沈明钢,杨利坡,等. 冷轧宽带钢板形控制降维功效继承调控方法[J]. 钢铁,2016,51(1):70.(SONG Lei,SHEN Ming-gang,YANG Li-po,et al. Shape control dimensionality reduction efficiency inherited regulation method of cold rolling wide strip[J]. Iron and Steel,2016,51(1):70.) [11] WU Z,HUANG N E. Ensemble empirical mode decomposition:A noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis,2009,1(1):1. [12] CHANG K M. Ensemble empirical mode decomposition for high frequency ECG noise reduc-tion[J]. Biomedizinische Technik,2010,55(4):193. [13] BI F,LI L,ZHANG J,et al. Source identification of gasoline engine noise based on con-tinuous wavelet transform and EEMD-Robust ICA[J]. Applied Acoustics,2015,100:34. [14] Lee D H,Ahn J H,Koh B H. Fault detection of bearing systems through EEMD and opti-mization algorithm[J]. Sensors,2017,17:2477. [15] 任新意,王松涛,高慧敏,等. 冷连轧机ESS辊型板形控制性能分析[J]. 钢铁,2018,53(3):50.(REN Xin-yi,WANG Song-tao,GAO Hui-min,et al. Analysis on shape control performance of cold tandem mill with ESS roll profile[J]. Iron and Steel,2018,53(3):50.) [16] 何小丽,王崇. 冷轧机组闭环板形控制策略研究[J]. 冶金自动化,2018,42(2):44.(HE Xiao-li,WANG Chong. Study on closed-loop shape control strategy of cold colling[J]. Metallurgical Industry Automation,2018,42(2):44.) [17] 张殿华,刘佳伟,王军生,等. 带钢冷连轧板形功效系数自学习计算模型[J]. 钢铁,2010,45(3):52.(ZHANG Dian-hua,LIU Jia-wei,WANG Jun-sheng,et al. Self-learning model of cold-strip mill based on the actuator efficiency factor of shape control[J]. Iron and Steel,2010,45(3):52.) [18] WANG Q L,SUN J,LI X,et al. Numerical and experimental analysis of strip cross-directional control andflatness prediction for UCM cold rolling mill[J]. Journal of Manufacturing Processes,2018,34:637. [19] 梁勋国,王业科. 六辊冷轧机功效系数的研究[J]. 轧钢,2013,30(5):1.(LIANG Xun-guo,WANG Ye-ke. Study on the actuator efficiency of six-high cold rolling mill[J]. Steel Rolling,2013,30(5):1.) [20] HUANG N E,SHEN Z,LONG S R,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A-Mathematical Physical and Engineering Sciences,1998,454:903. [21] Erturk A,Gullu M K,Erturk S. Hyperspectral image classification using empirical mode decomposition with spectral gradient enhancement[J]. IEEE Transactions on Geoscience and Remote Sensing,2013,51(5):2787. [22] Boudraa A O,Cexus J C. EMD-based signal filtering[J]. IEEE Transactions on Instrumenta-tion and Measurement,2007,56(6):2196.
2021, Vol. 56 
