Abnormality Monitoring in the Blast Furnace Ironmaking Process Based on Stacked Dynamic Target-Driven Denoising Autoencoders

Accurate monitoring of abnormalities is of great significance to the stable operation of the blast furnace ironmaking process. This article proposes a data-driven model to accurately monitor the abnormal conditions of blast furnaces. Generally, data-driven models primarily rely on feature extraction...

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Published in:	IEEE transactions on industrial informatics Vol. 18; no. 3; pp. 1854 - 1863
Main Authors:	Jiang, Ke, Jiang, Zhaohui, Xie, Yongfang, Pan, Dong, Gui, Weihua
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.03.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Abnormalities Blast furnace (BF) Blast furnace practice Blast furnaces Data models Deep learning denoising autoencoder (DAE) Design factors dynamic target-driven denoising autoencoder (D-DAE) Feature extraction Iron Ironmaking Machine learning Monitoring Noise reduction process monitoring
ISSN:	1551-3203, 1941-0050
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Abstract	Accurate monitoring of abnormalities is of great significance to the stable operation of the blast furnace ironmaking process. This article proposes a data-driven model to accurately monitor the abnormal conditions of blast furnaces. Generally, data-driven models primarily rely on feature extraction from high-dimensional raw data. Recently, deep learning networks have been developed and considered a promising technology in extracting high-level abstract features. However, most of these networks cannot capture deep target-related features for abnormality monitoring. Thus, this article proposes a novel stacked dynamic target-driven denoising autoencoder for layer-by-layer hierarchical feature representation, and the dynamic relationship between samples and targets is described by dynamic factors. Then, we design a corresponding target-driven reconstruction loss function to pretrain the deep network successively. Experimental results in an ironmaking plant demonstrate the effectiveness and feasibility of the proposed method.
AbstractList	Accurate monitoring of abnormalities is of great significance to the stable operation of the blast furnace ironmaking process. This article proposes a data-driven model to accurately monitor the abnormal conditions of blast furnaces. Generally, data-driven models primarily rely on feature extraction from high-dimensional raw data. Recently, deep learning networks have been developed and considered a promising technology in extracting high-level abstract features. However, most of these networks cannot capture deep target-related features for abnormality monitoring. Thus, this article proposes a novel stacked dynamic target-driven denoising autoencoder for layer-by-layer hierarchical feature representation, and the dynamic relationship between samples and targets is described by dynamic factors. Then, we design a corresponding target-driven reconstruction loss function to pretrain the deep network successively. Experimental results in an ironmaking plant demonstrate the effectiveness and feasibility of the proposed method.
Author	Xie, Yongfang Gui, Weihua Jiang, Ke Jiang, Zhaohui Pan, Dong
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SubjectTerms	Abnormalities Blast furnace (BF) Blast furnace practice Blast furnaces Data models Deep learning denoising autoencoder (DAE) Design factors dynamic target-driven denoising autoencoder (D-DAE) Feature extraction Iron Ironmaking Machine learning Monitoring Noise reduction process monitoring
Title	Abnormality Monitoring in the Blast Furnace Ironmaking Process Based on Stacked Dynamic Target-Driven Denoising Autoencoders
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