在未来工厂平台上采集的模拟与多模态制造数据集
Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform
AI总结：
本研究发布了两个在Future Factories Lab采集的工业级制造数据集，含带缺陷的模拟与多模态时间序列数据，旨在填补制造领域异常数据集稀缺的空白，为制造业AI模型研发提供基础。
摘要：
本文介绍了两个工业级数据集，它们于2023年12月11日至12日在南卡罗来纳大学的Future Factories Lab（未来工厂实验室）采集完成。这些数据集由一条制造装配线生成，该装配线在执行器、控制机制和传感器方面均采用工业标准。两个数据集通过连续运行装配线30小时（经轻微过滤）并采集系统各处配备的传感器数据同步生成。在运行过程中，研究人员还通过手动移除最终装配所需零件的方式，在装配作业中引入了缺陷。生成的数据集包含一个时间序列模拟数据集，以及另一个时间序列多模态数据集——后者除模拟数据外还包含系统的图像。生成这些数据集的目标是为推动制造智能化研究提供工具。真实制造数据集本就稀缺，带有异常或缺陷的数据集更是少之又少。因此，这些数据集有望填补这一空白，为研究人员构建和训练适用于制造业的Artificial Intelligence（人工智能）模型提供基础。最后，这些数据集是Future Factories Lab发布的首批数据，后续可进一步调整以满足更多研究人员的需求。
英文摘要：
Two industry-grade datasets are presented in this paper that were collected at the Future Factories Lab at the University of South Carolina on December 11th and 12th of 2023. These datasets are generated by a manufacturing assembly line that utilizes industrial standards with respect to actuators, control mechanisms, and transducers. The two datasets were both generated simultaneously by operating the assembly line for 30 consecutive hours (with minor filtering) and collecting data from sensors equipped throughout the system. During operation, defects were also introduced into the assembly operation by manually removing parts needed for the final assembly. The datasets generated include a time series analog dataset and the other is a time series multi-modal dataset which includes images of the system alongside the analog data. These datasets were generated with the objective of providing tools to further the research towards enhancing intelligence in manufacturing. Real manufacturing datasets can be scarce let alone datasets with anomalies or defects. As such these datasets hope to address this gap and provide researchers with a foundation to build and train Artificial Intelligence models applicable for the manufacturing industry. Finally, these datasets are the first iteration of published data from the future Factories lab and can be further adjusted to fit more researchers needs moving forward.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
