FactoryNet: Multi-Machine Industrial Sensor Data for Anomaly Detection

A large-scale, machine-learning-ready foundation dataset for industrial robotics and manufacturing anomaly detection. It unifies high-frequency physical data from completely different machines, addressing historical siloing of industrial datasets. The dataset was created by Forgis and was last updated on 2026-05-08.

Use Cases

• Training anomaly detection models based on unified multi-machine sensor data.
• Benchmarking industrial ML algorithms on a standardized, large-scale dataset.
• Developing cross-machine fault prediction systems based on high-frequency physical data.

Strengths

• Designed as a large-scale, machine-learning-ready foundation dataset.
• Unifies data from completely different machines to address siloed industrial data formats.
• Contains high-frequency physical data from industrial sources.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and license are unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Forgis on Hugging Face.

Collection Method

Forged from massive, high-frequency physical datasets from different industrial machines.

Freshness

Last updated 2026-05-08 11:58:21.