带钢表面粗糙度自适应滤波检测技术研究  

Research on adaptive filter detection technology of steel strip surface roughness

作　　者：汤文杰[1] 瞿海霞[2] 牟战旗[1] TANG Wenjie;QU Haixia;MOU Zhanqi(Cold Rooling Plant,Baoshan Iron&Steel Co.,Ltd.,Shanghai 201900,China;Research Institute,Baoshan Iron&Steel Co.,Ltd.,Shanghai 201999,China)

机构地区：[1]宝山钢铁股份有限公司冷轧厂,上海201900 [2]宝山钢铁股份有限公司中央研究院,上海201999

出　　处：《宝钢技术》2020年第5期24-28,共5页Baosteel Technology

摘　　要：为了解决散射法中带钢表面粗糙度在线检测传统滤波方法的不足,提升带钢表面粗糙度参数辨识的准确度,研究冷轧带钢表面粗糙度在线检测回归平滑自适应滤波方法。研究结果表明,回归平滑自适应滤波方法通过对表面粗糙度在线检测初始参数采用稳健局部加权回归进行降噪预处理,建立核函数,实现权值根据历史数据与当前数据的相对位置进行更新;当邻近数据变化超出设定阈值ε=0.75μm时,自动减小平滑窗口宽度,实现粗糙度在线检测数据的自适应变步长回归平滑滤波。该方法提高了冷轧带钢表面粗糙度的检测精度,更好地满足了下游用户的要求。To address the shortcomings of traditional filtering methods that utilize the scattering method for the online detection of strip surface roughness and to improve the accuracy of the identification of strip surface roughness,a regression-smoothing adaptive-filtering method is investigated for use in the online detection of surface roughness in the cold-rolled strip.The results show that by the use of robust locally weighted regression to perform noise-reduction preprocessing of the initial parameters in the online detection of surface roughness,followed by the establishment of a kernel function,the regression-smoothing adaptive-filtering method can update the weight based on the relative positions of historical and current data.When the changes in neighboring data exceed the established threshold value of 0.75μm,the width of the smoothing window is automatically reduced,thereby realizing adaptive variable-step regression-smoothing filtering of online roughness detection data.By the use of this regression-smoothing adaptive-filtering method,the accuracy of detecting the surface roughness of a cold-rolled strip can be improved and the requirements of downstream users better satisfied.

关 键 词：表面粗糙度 在线检测 自适应滤波 

分 类 号：TG84[金属学及工艺—公差测量技术]