钢铁钒钛2025，Vol.46Issue(5)：13-22,10.DOI:10.7513/j.issn.1004-7638.2025.05.002
基于图像识别的高炉风口剩余使用寿命预测模型研究
Research on the remaining useful life estimation model of blast furnace tuyere based on image recognition
廖哲晗 1武建龙 2黄俊杰 3郭宏烈 3徐健4
作者信息
- 1. 成都先进金属材料产业技术研究院股份有限公司,四川成都 610300||重庆大学材料科学与工程学院,重庆 400044
- 2. 重庆大学材料科学与工程学院,重庆 400044||首钢京唐钢铁联合有限责任公司炼铁作业部,河北唐山 063200||首钢集团有限公司技术研究院,北京 100043
- 3. 首钢京唐钢铁联合有限责任公司炼铁作业部,河北唐山 063200
- 4. 重庆大学材料科学与工程学院,重庆 400044
- 折叠
摘要
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 dam-aged 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 dis-crete 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.There-fore,BVT-RULNet model demonstrates high accuracy and strong generalization for tuyere RUL classi-fication and prediction,providing an effective solution for intelligent monitoring of blast furnace tuyere condition.关键词
高炉/集成学习策略/风口图像数据集/风口剩余使用寿命Key words
blast furnace/ensemble learning strategy/tuyere images dataset/tuyere remaining useful life分类
信息技术与安全科学引用本文复制引用
廖哲晗,武建龙,黄俊杰,郭宏烈,徐健..基于图像识别的高炉风口剩余使用寿命预测模型研究[J].钢铁钒钛,2025,46(5):13-22,10.
