State Prediction Method for A-Class Insulation Board Production Line Based on Transfer Learning

Yong Wang, Hui Wang, Xiaoqiang Guo, Xinhua Liu and Xiaowen Liu ()
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Yong Wang: School of Electrical Engineering, China University of Mining and Technology, Xuzhou 221000, China
Hui Wang: School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221000, China
Xiaoqiang Guo: School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221000, China
Xinhua Liu: School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221000, China
Xiaowen Liu: School of Electrical Engineering, China University of Mining and Technology, Xuzhou 221000, China

Mathematics, 2022, vol. 10, issue 20, 1-15

Abstract: It is essential to determine the running state of a production line to monitor the production status and make maintenance plans. In order to monitor the real-time running state of an A-class insulation board production line conveniently and accurately, a novel state prediction method based on deep learning and long short-term memory (LSTM) network is proposed. The multiple layers of the Res-block are introduced to fuse local features and improve hidden feature extraction. The transfer learning strategy is studied and the improved loss function is proposed, which makes the model training process fast and stable. The experimental results show that the proposed Res-LSTM model reached 98.9% prediction accuracy, and the average R 2 -score of the industrial experiments can reach 0.93. Compared with other mainstream algorithms, the proposed Res-LSTM model obtained excellent performance in prediction speed and accuracy, which meets the needs of industrial production.

Keywords: transfer learning; state prediction for production line; LSTM; domain adaptation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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