用视觉+传感器数据实时预测轧钢机故障，减少停产损失。
Process Integrated Computer Vision for Real-Time Failure Prediction in Steel Rolling Mill
- 用工业相机实时捕捉设备运行画面，结合深度学习分析异常
- 通过多源数据融合定位故障位置，提前预警避免停机
- 部署在中央服务器，不增加产线控制负担，适合大规模应用
我们报告了一项基于机器视觉的异常检测系统在轧钢车间长期部署的研究。该系统利用工业摄像头实时监控生产线上的设备运行、对齐状态及热钢坯运动。视频流由中心化视频服务器通过深度学习模型处理，实现设备故障与生产中断的早期预测，从而降低非计划停机成本。基于服务器的推理减少了对工业过程控制系统（PLCs）的计算压力，支持在无需额外资源的情况下跨产线规模化部署。通过联合分析数据采集系统与视觉输入，系统可识别故障位置及其可能原因，提供可操作的维护建议。该集成方法提升了工业制造环境中的运营可靠性、生产效率与盈利能力。
原文摘要 · Abstract (English)
We present a long-term deployment study of a machine vision-based anomaly detection system for failure prediction in a steel rolling mill. The system integrates industrial cameras to monitor equipment operation, alignment, and hot bar motion in real time along the process line. Live video streams are processed on a centralized video server using deep learning models, enabling early prediction of equipment failures and process interruptions, thereby reducing unplanned breakdown costs. Server-based inference minimizes the computational load on industrial process control systems (PLCs), supporting scalable deployment across production lines with minimal additional resources. By jointly analyzing sensor data from data acquisition systems and visual inputs, the system identifies the location and probable root causes of failures, providing actionable insights for proactive maintenance. This integrated approach enhances operational reliability, productivity, and profitability in industrial manufacturing environments.
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