首个工业时序数据通用预训练数据集，支持跨设备零样本迁移。
FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Models
- 提出S-E-F-C统一表征框架，将各类工控系统映射到相同语义空间。
- 涵盖5100万条数据、27类异常，实现跨设备零样本迁移与高效检测。
- 适合工业AI研究者、智能制造开发者使用，推动工业大模型发展。
我们提出首个面向工业时序数据的通用预训练语料库FactoryNet。该数据集包含5100万条数据，覆盖23000次端到端任务执行（其中13300次为真实数据，9800次为合成数据），涉及六种设备形态，采用统一的S-E-F-C（设定值、努力值、反馈值、上下文）表征架构，实现鲁棒的零样本跨设备迁移与高参数效率的异常检测。数据集涵盖27类标注异常类型，包含健康基线和反事实对，覆盖机器人操作与加工领域。跨设备迁移实验表明，在考虑偏倚的评估指标下，模型在选定源-目标对上展现出公平的迁移能力；24个符合架构的信号即达到与高维基线相当的异常检测性能。FactoryNet作为持续增长的多设备数据集，旨在推动工业基础模型的发展。
原文摘要 · Abstract (English)
We introduce the first universal pretraining corpus for industrial time-series data: FactoryNet. 51M datapoints across 23k end-to-end task executions (13.3k real, 9.8k synthetic) on six embodiments, unified by a shared schema that enables robust zero-shot cross-embodiment transfer and highly parameter-efficient anomaly detection. We introduce a novel schema: Setpoint, Effort, Feedback, Context (S-E-F-C) underlying the whole pipeline that maps any actuated system into a common representational frame. The corpus spans 27 annotated anomaly types alongside healthy baselines and counterfactual pairs across robotic manipulation and machining domains. Cross-embodiment transfer experiments yield positive results: under bias-aware metrics our model demonstrates fair cross-embodiment transfer capabilities on the evaluated source-target pair, while 24 schema-aligned signals achieves competitive anomaly detection performance compared to high-dimensional baselines. We release FactoryNet as a growing, multi-embodiment dataset to drive progress toward industrial foundation models.
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