Next Article in Journal
A Circular-Based Reference Point Extraction Method for Correcting the Alignment of Round Parts
Previous Article in Journal
Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based Approach
 
 
Article

Sintering Quality Prediction Model Based on Semi-Supervised Dynamic Time Feature Extraction Framework

State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
*
Author to whom correspondence should be addressed.
Academic Editor: Jiayi Ma
Sensors 2022, 22(15), 5861; https://doi.org/10.3390/s22155861 (registering DOI)
Received: 2 July 2022 / Revised: 25 July 2022 / Accepted: 3 August 2022 / Published: 5 August 2022
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. View Full-Text
Keywords: LSTM; semi-supervised learning; FeO content; soft sensor; encoder-decoder; dynamic feature extraction LSTM; semi-supervised learning; FeO content; soft sensor; encoder-decoder; dynamic feature extraction
Show Figures

Figure 1

MDPI and ACS Style

Li, Y.; Yang, C.; Sun, Y. Sintering Quality Prediction Model Based on Semi-Supervised Dynamic Time Feature Extraction Framework. Sensors 2022, 22, 5861. https://doi.org/10.3390/s22155861

AMA Style

Li Y, Yang C, Sun Y. Sintering Quality Prediction Model Based on Semi-Supervised Dynamic Time Feature Extraction Framework. Sensors. 2022; 22(15):5861. https://doi.org/10.3390/s22155861

Chicago/Turabian Style

Li, Yuxuan, Chunjie Yang, and Youxian Sun. 2022. "Sintering Quality Prediction Model Based on Semi-Supervised Dynamic Time Feature Extraction Framework" Sensors 22, no. 15: 5861. https://doi.org/10.3390/s22155861

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop