【摘要】 高炉风口作为炉内热量的主要来源，其状态检测目前主要依赖人工经验判断，常导致损坏风口更换不及时，造成不必要的停产检修。为解决上述问题，提出了一种专为风口剩余使用寿命（Remaining Useful Life, RUL）预测的机器学习模型BVT-RULNet，该模型采用集成学习（Ensemble Learning, EL）策略，集成三个结构相同的基分类器，每个基分类器均包含一个VGG16卷积神经网络（Convolutional Neural Network, CNN）前端和一个视觉Transformer(Vision Transformer, ViT)模块。在模型训练过程中，采用基于风口完整生命周期图像构建的离散RUL数据集，在测试过程中，则采用独立的测试数据集。结果表明，模型在测试数据集上的评估指标表现优异，准确率、精确率、召回率和F1分数分别达到85.14%、84.70%、84.59%和84.64%，均优于对比模型。因此，BVT-RULNet模型在风口RUL分类识别与预测方面具有较高的准确性和泛化能力，为高炉风口状态智能监测提供了有效的解决方案。更多还原

【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 reaching85.14%, 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.更多还原