基于双通道特征融合的热连轧厚度预测补偿

Thickness prediction and compensation for hot tandem rolling based on dual-channel feature fusion

针对热连轧轧制受油膜厚度、轧辊偏心等因素影响而造成精度降低的问题,提出一种基于双通道特征融合的热连轧板带厚度误差预测模型。模型由时空特征提取和多尺度特征提取两部分组成:时空特征提取部分是基于相邻机架板带厚度空间的相关性构建了前置时空矩阵(FSTM),通过卷积长短期记忆网络(Conv-LSTM)提取FSTM的时空关联特征;多尺度特征提取部分是采用离散小波变换(DWT)对当前机架轧制厚度数据进行分解,得到趋势项数据和细节项数据,并采用差分自回归移动平均模型-长短期记忆网络(ARIMA-LSTM)和双向长短期记忆网络(BiLSTM)分别对趋势项数据和细节项数据进行特征提取。将上述特征融合后输入全连接层进行回归预测,得到热连轧板带厚度误差预测值。实验结果表明:双通道特征融合模型能有效提高厚度误差预测精度,验证了模型的有效性。

For the problem of accuracy reduction in hot tandem rolling caused by factors such as oil film thickness and roll eccentricity,a thickness error prediction model for hot tandem rolling strips based on dual-channel feature fusion was proposed,which was composed of two parts,namely,spatiotemporal feature extraction and multi-scale feature extraction.For the spatiotemporal feature extraction part,a Frontal Spatio-Temporal Matrix(FSTM)was constructed based on the spatial correlation of adjacent rack plate thicknesses,and the spati-otemporal correlation features of the FSTM were extracted by the Convolutional Long Short-Term Memory Network(Conv-LSTM).For the multi-scale feature extraction part,the current rack rolling thickness data was decomposed by Discrete Wavelet Transform(DWT)to ob-tain trend item data and detail item data,and Autoregressive Integrated Moving Average Long Short-Term Memory Network(ARIMA-LSTM)and Bi-directional Long Short-Term Memory Network(BiLSTM)were used to extract features from the trend item data and detail item data,respectively.The above features were fused and input into the fully connected layer for regression prediction,the thickness er-ror prediction value of the hot tandem rolling strip was obtained.Experimental results show that the dual-channel feature fusion model ef-fectively improves the accuracy of thickness error prediction,validating the effectiveness of the model.