Identification of Abnormal Conditions for Fused Magnesium Melting Process Based on Deep Learning and Multisource Information Fusion

• Published in: IEEE transactions on industrial electronics (1982) Vol. 69; no. 3; pp. 3017 - 3026
• Main Authors: Zhou, Ping, Gao, Benhua, Wang, Shu, Chai, Tianyou
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abnormal condition; Artificial neural networks; Data integration; Deep learning; Feature extraction; Furnaces; fused magnesium furnace (FMF); Generative adversarial networks; generative adversarial networks (GAN); Image recognition; Machine learning; Magnesite; Magnesium carbonate; Melt temperature; Melting; multisource information fusion; Overheating; Production; Smelting; Support vector machines; working-condition detection
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2021.3070512

Fused magnesium furnace (FMF) is the vital equipment for magnesia refractory production. The melting process of FMF is subject to melting temperature, magnesite quality and composition, and much more dynamic factors, which are prone to abnormal conditions such as overheating, abnormal exhausting, semi-molten, and affect the stability and safety of the production seriously. Due to the difficulties of effectively monitoring and accurately identifying the abnormal conditions with the existing methods, this article proposes a novel method for identifying the abnormal conditions of FMF based on deep learning and multi-information fusion. First, the Wasserstein distance based generative adversarial networks with gradient penalty is developed to solve the problem of obtaining overheating condition image samples with obvious visual characteristics. Then, the image-based deep convolutional neural network model is established to recognize the abnormal condition images and extract image features whose training set is the combination of the generated images and the original images. Finally, based on multisource information fusion, the traditional process current data are fused with the extracted image features, and a support vector machine model is established to complete the classification and identification of the FMF working conditions. Various experiments on actual industrial data show that the proposed method is effective and advanced.