Regular Papers

International Journal of Control, Automation, and Systems

Published online January 18, 2024

https://doi.org/10.1007/s12555-022-1105-5

© The International Journal of Control, Automation, and Systems

Abnormal Condition Identification for the Electro-fused Magnesia Smelting Process Based on Condition-relevant Information

Yan Liu*, Zhenyu Liu, Fuli Wang, Yulu Xiong, Ruicheng Ma, and Fei Chu

Northeastern University

Abstract

To improve the accuracy of feature representation and abnormal condition identification, a new abnormal condition identification method, named integrating multiple binary neural networks based on condition-relevant information (CRI-MBNN), is presented for the electro-fused magnesia smelting process in this study. Firstly, the features related to each specific abnormal condition, which is named condition-relevant information (CRI), are analyzed and extracted from the multi-source heterogeneous information with the help of limited and consensus domain knowledge. Then, the CRI is fused at the feature-level to provide a comprehensive representation of each abnormal condition. Furthermore, for each abnormal condition, a binary neural network (BNN) is established based on the fused feature. They are further integrated according to the frequency of each condition in the actual production process to form the final abnormal condition identification network, i.e., CRI-MBNN. Finally, the effectiveness and feasibility of the proposed CRI-MBNN are verified by the electro-fused magnesia smelting process.

Keywords Abnormal condition identification
condition-relevant information
electro-fused magnesia smelting process
feature fusion
multi-source heterogeneous information

Article

Regular Papers

International Journal of Control, Automation, and Systems -0001; ():

Published online November 30, -0001 https://doi.org/10.1007/s12555-022-1105-5

Copyright © The International Journal of Control, Automation, and Systems.

Abnormal Condition Identification for the Electro-fused Magnesia Smelting Process Based on Condition-relevant Information

Yan Liu*, Zhenyu Liu, Fuli Wang, Yulu Xiong, Ruicheng Ma, and Fei Chu

Northeastern University

Abstract

To improve the accuracy of feature representation and abnormal condition identification, a new abnormal condition identification method, named integrating multiple binary neural networks based on condition-relevant information (CRI-MBNN), is presented for the electro-fused magnesia smelting process in this study. Firstly, the features related to each specific abnormal condition, which is named condition-relevant information (CRI), are analyzed and extracted from the multi-source heterogeneous information with the help of limited and consensus domain knowledge. Then, the CRI is fused at the feature-level to provide a comprehensive representation of each abnormal condition. Furthermore, for each abnormal condition, a binary neural network (BNN) is established based on the fused feature. They are further integrated according to the frequency of each condition in the actual production process to form the final abnormal condition identification network, i.e., CRI-MBNN. Finally, the effectiveness and feasibility of the proposed CRI-MBNN are verified by the electro-fused magnesia smelting process.

Keywords: Abnormal condition identification
condition-relevant information
electro-fused magnesia smelting process
feature fusion
multi-source heterogeneous information

IJCAS
February 2024

Vol. 22, No. 2, pp. 347~729

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IJCAS

eISSN 2005-4092
pISSN 1598-6446