基于迁移学习和多方面特征提取的冷轧轧制力预测

Prediction on cold rolling force based on transfer learning and multi-aspect feature extraction

针对冷轧机各机架轧制工况复杂,实际采集的工艺数据存在分布差异,导致小数量样本下轧制力预测模型对不同机架预测时出现精度降低的问题,提出一种基于迁移学习和多方面特征提取的轧制力预测模型。该方法通过迁移学习将建立的轧制力预测基准模型参数迁移至目标机架,从而实现小数量样本下不同机架的轧制力快速预测。预测时,利用源域数据建立了Inception-LSTM-Attention轧制力预测基准模型,基准模型结合了Inception网络模块的空间特征提取能力和LSTM的时序特征预测能力,同时引入注意力机制Attention调节神经网络学习到的信息向量权重,基准模型预测精度高达98.8%。然后将基准模型部分网络模块冻结迁移至目标域模型,并对模型参数进行微调,建立了最终的轧制力预测模型。实验结果表明,小数量样本下采用迁移学习方法建立的模型在预测精度方面高于传统神经网络预测模型。

Aiming at the problem of reduced prediction accuracy of rolling force prediction model under small number of samples when pre-dicting the rolling force of different frames,which is caused by the complex rolling condition of each frame for cold rolling mill to make the distribution of actual collected process data different,a rolling force prediction model based on transfer learning and multi-aspect feature ex-traction was proposed.This method could transfer the parameters of established rolling force prediction benchmark model to the target frame by transfer learning to realize the rapid prediction of rolling force in different frames with small number of samples.When predicting,Incep-tion-LSTM-Attention rolling force prediction benchmark model was established by using the source domain data,which combined the spatial feature extraction ability of Inception network module and the temporal feature prediction ability of LSTM.At the same time,the attention mechanism Attention was introduced to adjust the weight of information vector learned by the neural network,and the prediction accuracy of the benchmark model was as high as 98.8%.Then,some network modules of the benchmark model were frozen and transfered to the target domain model,and the model parameters were fine-tuned to establish the final rolling force prediction model.The experimental results show that the prediction accuracy of the model established based on the transfer learning method under small number of samples is higher than that of the traditional neural network prediction model.