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 ro...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,in this paper,a regression-smoothing adaptive-filtering method was 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. 75 μm,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.展开更多
文摘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,in this paper,a regression-smoothing adaptive-filtering method was 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. 75 μm,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.
