README.md

---

license: cc-by-4.0
task_categories:
  - time-series-classification
  - question-answering
tags:
  - robotics
  - manufacturing
  - anomaly-detection
  - physical-ai
  - industrial
  - tool-wear
  - cnc
size_categories:
  - 1M<n<10M
language:
  - en
pretty_name: FactoryNet Hackathon Dataset
---


# FactoryNet Hackathon Dataset

A unified multi-robot time-series dataset for industrial anomaly detection and Physical AI research.

## Dataset Description

FactoryNet unifies multiple industrial operation datasets into a common schema for training anomaly detection and reasoning models. This release includes:

| Dataset | Machine | Task | Episodes | Rows | Faults |
|---------|---------|------|----------|------|--------|
| **AURSAD** | UR3e (6-DOF cobot) | Screwdriving | 4,094 | 6.2M | 5 types |
| **voraus-AD** | Yu-Cobot (6-DOF) | Pick-and-place | 2,122 | 2.3M | 12 types |
| **NASA Milling** | CNC (3-axis) | Milling | 167 | 1.5M | Tool wear |

**Total: 6,383 episodes, 10M+ rows**

## FactoryNet Schema

All datasets are converted to a unified schema with causal structure:

```

Intent (setpoint) → Action (effort) → Outcome (feedback)

```

### Core Columns (Tier 1 - Universal)
```python

setpoint_pos_0..N    # Commanded joint positions (rad) - for robots

setpoint_doc         # Depth of cut (mm) - for CNC

setpoint_feed        # Feed rate (mm/rev) - for CNC

effort_torque_0..N   # Motor torque/current (Nm / A)

feedback_pos_0..N    # Actual joint positions (rad)

timestamp            # Seconds since episode start

```

### Common Columns (Tier 2)
```python

setpoint_vel_*       # Commanded velocities

feedback_vel_*       # Actual velocities

effort_force_x/y/z   # End-effector forces

aux_vibration_*      # Vibration sensors

aux_acoustic_*       # Acoustic emission

ctx_temp_*           # Joint temperatures

```

## Quick Start

```python

from datasets import load_dataset



# Load AURSAD (robot screwdriving)

ds = load_dataset("Forgis/factorynet-hackathon", data_dir="aursad")

df = ds['train'].to_pandas()

print(f"AURSAD: {df['episode_id'].nunique()} episodes")



# Load voraus-AD (robot pick-and-place)

ds = load_dataset("Forgis/factorynet-hackathon", data_dir="voraus")

df = ds['train'].to_pandas()

print(f"voraus-AD: {df['episode_id'].nunique()} episodes")



# Load NASA Milling (CNC tool wear)

ds = load_dataset("Forgis/factorynet-hackathon", data_dir="nasa_milling")

df = ds['train'].to_pandas()

print(f"NASA Milling: {df['episode_id'].nunique()} episodes")

```

## Minimum Viable Episode (MVE)

Every episode in FactoryNet satisfies the **Minimum Viable Episode** constraint:
- ≥1 setpoint signal (commanded intent)
- ≥1 effort signal (motor response)

This enables causal analysis: if `effort` doesn't follow `setpoint`, something is wrong.

## Fault Types

| Code | Description | Dataset |
|------|-------------|---------|
| `normal` | Normal operation | All |
| `stiff_joint` | Increased joint friction | AURSAD |
| `collision` | Contact with obstacle | voraus-AD |
| `grip_failure` | Gripper malfunction | voraus-AD |
| `missing_part` | Expected part absent | AURSAD |
| `tool_wear` | Progressive degradation | NASA Milling |

## File Structure

```

Forgis/factorynet-hackathon/

├── aursad/

│   └── aursad_factorynet.parquet        # 1.5 GB - UR3e time series

├── voraus/

│   └── voraus_ad_100hz_factorynet.parquet  # 461 MB - Yu-Cobot time series

├── nasa_milling/

│   ├── nasa_milling_factorynet.parquet  # 15 MB - CNC time series

│   └── nasa_milling_metadata.json       # Episode metadata with wear labels

├── metadata/

│   ├── aursad_metadata.json             # 78 MB - AURSAD episode metadata

│   └── voraus_metadata.json             # 53 MB - voraus episode metadata

├── schema.json                          # FactoryNet schema reference

├── factorynet_loader.py                 # Easy-load Python utility

└── README.md

```

## Use Cases

1. **Anomaly Detection**: Train classifiers to detect faulty operations
2. **Fault Diagnosis**: Identify which component/joint is failing
3. **Remaining Useful Life**: Predict when tool/component will fail (NASA Milling)
4. **Sim2Real Transfer**: Use real data to calibrate simulators
5. **Robot Q&A**: Answer natural language questions about robot state

## Citation

If you use this dataset, please cite:

```bibtex

@dataset{factorynet2026,

  title={FactoryNet: A Unified Dataset for Industrial Robot Anomaly Detection},

  author={Forgis AI},

  year={2026},

  publisher={HuggingFace},

  url={https://huggingface.co/datasets/Forgis/factorynet-hackathon}

}

```

## Source Datasets

This dataset unifies and standardizes:
- **AURSAD**: [Zenodo](https://zenodo.org/records/4487073) - CC BY 4.0
- **voraus-AD**: [GitHub](https://github.com/vorausrobotik/voraus-ad-dataset) - MIT License
- **NASA Milling**: [NASA Open Data](https://data.nasa.gov/dataset/milling-wear) - Public Domain

## License

CC BY 4.0 - Free to use with attribution.

## Contact

- Forgis AI: hackathon@forgis.com
- Physical AI Hackathon: Zurich, Feb 28 - Mar 1, 2026