基于BSCWEformer的退火炉内分组式辊速序列预测

Predicting Grouped Rollers Speed Series in Annealing Furnace Based on BSCWEformer

退火炉内带钢的长度受到温度、张力等因素的影响而变化,导致辊的转速改变以及焊缝位置的不确定,从而威胁生产安全.为了准确预测辊的转速以计算焊缝的实时位置,本文提出基于带状稀疏柯西自注意力的BSCWEformer (banded sparse Cauchy weight enhanced Transformer)模型.模型采用带状稀疏的、使用基于相对位置计算的柯西分布权重值增强的自注意力结构,在提高相邻输入序列的重要性的同时,将自注意力的复杂度由二次方降低为线性.通过实际生产数据进行实验,并与LogSparse Transformer、Transformer、RNMT+等模型进行对比,得出本文所提出的BSCWEformer模型在退火炉内分组式辊速序列预测任务上具有较高的预测精度.

The length of the strip steel in the annealing furnace is affected by temperature,tension,and other factors,resulting in changes in roller speed and uncertainty in weld position and threatening production safety.To accurately predict roller speed,this study proposes the banded sparse Cauchy weight enhanced Transformer(BSCWEformer) model.The model adopts a banded sparse self-attention structure enhanced by Cauchy distribution weight values calculated from relative positions,which improves the importance of adjacent input sequences and reduces the complexity of self-attention from quadratic to linear.Through experiments with actual production data and comparison with LogSparse Transformer,Transformer,RNMT+,and other models,the BSCWEformer model shows higher accuracy in predicting grouped roller speed series.