FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Models
This work provides the first large-scale, multi-embodiment industrial time-series dataset to facilitate the development of foundation models for anomaly detection and cross-embodiment transfer, addressing a lack of unified data in industrial settings.
FactoryNet introduces a universal pretraining corpus for industrial time-series data with 51M datapoints across 23k task executions, enabling robust zero-shot cross-embodiment transfer and parameter-efficient anomaly detection, achieving competitive performance compared to high-dimensional baselines.
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.