State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel). 2022 Aug 5;22(15):5861. doi: 10.3390/s22155861.
In the sintering process, it is difficult to obtain the key quality variables in real time, so there is lack of real-time information to guide the production process. Furthermore, these labeled data are too few, resulting in poor performance of conventional soft sensor models. Therefore, a novel semi-supervised dynamic feature extraction framework (SS-DTFEE) based on sequence pre-training and fine-tuning is proposed in this paper. Firstly, based on the DTFEE model, the time features of the sequences are extended and extracted. Secondly, a novel weighted bidirectional LSTM unit (BiLSTM) is designed to extract the latent variables of original sequence data. Based on improved BiLSTM, an encoder-decoder model is designed as a pre-training model with unsupervised learning to obtain the hidden information in the process. Next, through model migration and fine-tuning strategy, the prediction performance of labeled datasets is improved. The proposed method is applied in the actual sintering process to estimate the FeO content, which shows a significant improvement of the prediction accuracy, compared to traditional methods.
在烧结过程中，很难实时获得关键质量变量，因此缺乏实时信息来指导生产过程。此外，这些标记数据太少，导致传统软传感器模型的性能不佳。因此，本文提出了一种基于序列预训练和微调的新型半监督动态特征提取框架（SS-DTFEE）。首先，基于 DTFEE 模型，扩展和提取了序列的时间特征。其次，设计了一种新颖的加权双向 LSTM 单元（BiLSTM），用于提取原始序列数据的潜在变量。基于改进的 BiLSTM，设计了一个编码器-解码器模型作为无监督学习的预训练模型，以获取过程中的隐藏信息。接下来，通过模型迁移和微调策略，提高了标记数据集的预测性能。该方法应用于实际的烧结过程，以估计 FeO 含量，与传统方法相比，预测精度有了显著提高。
