基于残差卷积自注意力神经网络的铝电解过热度识别方法

过热度是反映铝电解槽当前生产效率的重要指标，由于过热度难以在线实时测量，本文提出一种基于残差卷积自注意力神经网络的过热度识别方法.针对铝电解生产过程数据为时间序列数据且具有多源异构特性，设计异构数据的同构表示方法.在此基础上建立残差卷积自注意力神经网络模型以提取同构时间序列数据的全局与局部特征.针对过热度数据标签少且类别分布不均匀问题，采用基于自动编码器的无监督预训练方法与加权交叉熵损失函数以提高过热度识别任务的性能.在基准数据集上进行仿真对比实验以验证本文所提方法的有效性，然后在只包含少量不平衡标签的铝电解过热度数据集上进行实验验证，结果表明本文构建的过热度识别模型相较与其他现有模型不仅提高了过热度识别准确率，而且在训练样本较少时保证了模型的泛化能力.

An Superheat Identification Method in Aluminium Electrolysis Based on Residual Convolutional Self-Attention Neural Network

Superheat is an important indicator to reflect the current production efficiency of aluminium electrolytic cells. Due to the difficulty of superheat online real-time measurement， this paper proposes a superheat identification method based on residual convolution self-attention neural network (RCSANN). As the production data in aluminium electrolysis process is time series data and featured with multi-source heterogeneous characteristics， the isomorphic representation method is designed for heterogeneous data. On this basis， the RCSANN superheat model is proposed to extract the global and local features of the isomorphic time series data. Aiming at the problem of few labels and uneven category distribution of superheat data， the unsupervised pre-training method based on auto-encoder and the weighted cross-entropy loss function are used to improve the performance of the superheat identification task. The validity of the proposed method is verified by simulation and comparison experiments on the benchmark dataset. Then， experiments are carried out on the dataset of superheat in aluminium electrolysis with only a few unbalanced labels. The results show that not only the accuracy of superheat identification is improved compared with other existing models， but also the generalization ability can be guaranteed under few training labeled-samples.