用振动数据实现钢渣流动实时监测，准确率达99.1%。
Industrial Steel Slag Flow Data Loading Method for Deep Learning Applications
- 融合卷积与LSTM的混合模型处理加速度计原始信号。
- 跨域测试准确率最高达99.10±0.30%，优于传统方法。
- 适合工业现场实时监控，可直接部署于钢铁生产流程。
钢水浇铸过程易因钢渣流动污染导致经济损失，精准识别钢渣流动状态至关重要。本研究提出一种基于工业炼钢厂振动数据的跨域诊断方法，用于识别不同阶段的钢渣流动。采用一维卷积神经网络与长短期记忆层相结合的混合深度学习模型，对加速度计采集的原始时域振动信号进行处理，并在真实跨域数据集划分下，于16个不同工况下进行性能评估。实验结果表明，结合均方根预处理与选择性嵌入数据加载策略的混合模型，在各类场景中均表现稳健，最高测试准确率达99.10±0.30%，显著优于标准一维卷积网络及其他加载方法。该方法为钢渣流动的实时监测提供了实用且可扩展的解决方案，有助于提升钢铁制造过程的可靠性与运行效率。
原文摘要 · Abstract (English)
Steel casting processes are vulnerable to financial losses due to slag flow contamination, making accurate slag flow condition detection essential. This study introduces a novel cross-domain diagnostic method using vibration data collected from an industrial steel foundry to identify various stages of slag flow. A hybrid deep learning model combining one-dimensional convolutional neural networks and long short-term memory layers is implemented, tested, and benchmarked against a standard one-dimensional convolutional neural network. The proposed method processes raw time-domain vibration signals from accelerometers and evaluates performance across 16 distinct domains using a realistic cross-domain dataset split. Results show that the hybrid convolutional neural network and long short-term memory architecture, when combined with root mean square preprocessing and a selective embedding data loading strategy, achieves robust classification accuracy, outperforming traditional models and loading techniques. The highest test accuracy of 99.10 +/- 0.30 demonstrates the method's capability for generalization and industrial relevance. This work presents a practical and scalable solution for real-time slag flow monitoring, contributing to improved reliability and operational efficiency in steel manufacturing.
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