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Dynamic Prediction of Abnormal Condition for Multiple Fused Magnesium Melting Processes Based on Video Continual Learning.pdf (194.29 kB)
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Dynamic Prediction of Abnormal Condition for Multiple Fused Magnesium Melting Processes Based on Video Continual Learning

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posted on 27.04.2022, 03:29 by Zhe ZhangZhe Zhang

Process industry is the pillar industry of national economy, particularly, the process of producing magnesia by fused magnesia furnace system is a typical category of process industry. Due to the complex smelting mechanism and changing production factors, abnormal working conditions often occur in fused magnesia furnace. The semi-molten condition is the most typical and harmful abnormal condition. In this paper, an adaptive pretraining-inference-dynamic training-validation semantic segmentation method based on industrial video is proposed for dynamic prediction of semi-molten condition of multiple fused magnesium furnaces. The experimental results show that compared with the prediction model without adaptive learning, the prediction performance of the adaptive learning model in this paper for multiple fused magnesium melting processes is significantly improved. 

  

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Email Address of Submitting Author

zhangzhe17@stumail.neu.edu.cn

Submitting Author's Institution

Northeastern University

Submitting Author's Country

China