两个多模态制造数据集，助力AI算法在真实产线环境测试
Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform V2
- 8小时连续采集产线模拟数据，融合传感器与摄像头信号
- 包含时序模拟数据和同步图像的多模态数据，支持算法验证
- 开源数据集，适合智能制造、工业AI研究者快速上手
本文介绍了在南卡罗来纳大学未来工厂实验室于2024年8月13日进行的8小时连续运行中获取的两个工业级数据集。数据涵盖通信协议、执行器、控制机制、换能器、传感器及摄像头等，通过集成与外部传感器采集，包括嵌入式执行器传感器和外置设备，并由高性能相机记录关键操作环节。此前一次30小时连续运行中已记录所有异常，后续实施维护以减少潜在错误与中断。所提数据集包括：(1) 时序模拟数据集；(2) 含同步系统数据与图像的多模态时序数据集。这些数据旨在支持制造流程的算法研究，无需重建物理环境即可测试新方法。数据集为开源，可直接用于人工智能模型训练，提升研究效率。
原文摘要 · Abstract (English)
This paper presents two industry-grade datasets captured during an 8-hour continuous operation of the manufacturing assembly line at the Future Factories Lab, University of South Carolina, on 08/13/2024. The datasets adhere to industry standards, covering communication protocols, actuators, control mechanisms, transducers, sensors, and cameras. Data collection utilized both integrated and external sensors throughout the laboratory, including sensors embedded within the actuators and externally installed devices. Additionally, high-performance cameras captured key aspects of the operation. In a prior experiment [1], a 30-hour continuous run was conducted, during which all anomalies were documented. Maintenance procedures were subsequently implemented to reduce potential errors and operational disruptions. The two datasets include: (1) a time-series analog dataset, and (2) a multi-modal time-series dataset containing synchronized system data and images. These datasets aim to support future research in advancing manufacturing processes by providing a platform for testing novel algorithms without the need to recreate physical manufacturing environments. Moreover, the datasets are open-source and designed to facilitate the training of artificial intelligence models, streamlining research by offering comprehensive, ready-to-use resources for various applications and projects.
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