Research on the remaining useful life estimation model of blast furnace tuyere based on image recognition

LIAO Zhehan; WU Jianlong; HUANG Junjie; GUO Honglie; XU Jian

doi:10.7513/j.issn.1004-7638.2025.05.002

Volume 46 Issue 5

Oct. 2025

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LIAO Zhehan, WU Jianlong, HUANG Junjie, GUO Honglie, XU Jian. Research on the remaining useful life estimation model of blast furnace tuyere based on image recognition[J]. IRON STEEL VANADIUM TITANIUM, 2025, 46(5): 13-22. doi: 10.7513/j.issn.1004-7638.2025.05.002

Citation:

LIAO Zhehan, WU Jianlong, HUANG Junjie, GUO Honglie, XU Jian. Research on the remaining useful life estimation model of blast furnace tuyere based on image recognition[J]. IRON STEEL VANADIUM TITANIUM, 2025, 46(5): 13-22. doi: 10.7513/j.issn.1004-7638.2025.05.002

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Research on the remaining useful life estimation model of blast furnace tuyere based on image recognition

doi: 10.7513/j.issn.1004-7638.2025.05.002

LIAO Zhehan^{1, 2
,},
WU Jianlong^{2, 3, 4},
HUANG Junjie³,
GUO Honglie³,
XU Jian²

1.
Chengdu Advanced Metal Materials Industry Technology Research Institute Co., Ltd., Chengdu 610300, Sichuan, China
2.
College of Materials Science and Engineering, Chongqing University, Chongqing 400044, China
3.
Ironmaking Department, Shougang Jingtang United Iron and Steel Co., Ltd., Tangshan 063200, Hebei, China
4.
Shougang Technology Research Institute, Beijing 100043, China

More Information

Received Date: 2025-07-31
Accepted Date: 2025-09-05
Rev Recd Date: 2025-09-04
Publish Date: 2025-10-30

Abstract

Abstract

As the primary source of in-furnace heat, the condition detection of blast furnace tuyeres mainly relies on manual experience currently. This practice often leads to delayed replacement of damaged tuyeres and unnecessary shutdown maintenance. To address the above issues, this paper proposes a machine learning model named BVT-RULNet, specifically designed to predict the Remaining Useful Life (RUL) of tuyeres. The model employs an Ensemble Learning (EL) strategy that integrates three base classifiers with identical architectures. Each base classifier consists of a VGG16 Convolutional Neural Network (CNN) frontend and a Vision Transformer (ViT) module. During model training, a discrete RUL dataset constructed based on the images covering the complete life cycle of the tuyere was used, and an independent test dataset was used during the evaluation process. Results show that the model achieves excellent metrics on the test set, with accuracy, precision, recall, and F1 score reaching 85.14%, 84.70%, 84.59%, and 84.64%, respectively, all outperforming the comparison models. Therefore, BVT-RULNet model demonstrates high accuracy and strong generalization for tuyere RUL classification and prediction, providing an effective solution for intelligent monitoring of blast furnace tuyere condition.
- blast furnace,
- ensemble learning strategy,
- tuyere images dataset,
- tuyere remaining useful life

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References(36)

References

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