| ---
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| license: cc-by-4.0
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| task_categories:
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| - time-series-classification
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| - question-answering
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| tags:
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| - robotics
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| - manufacturing
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| - anomaly-detection
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| - physical-ai
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| - industrial
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| - tool-wear
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| - cnc
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| size_categories:
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| - 1M<n<10M
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| language:
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| - en
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| pretty_name: FactoryNet Hackathon Dataset
|
| ---
|
|
|
| # FactoryNet Hackathon Dataset
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|
|
| A unified multi-robot time-series dataset for industrial anomaly detection and Physical AI research.
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|
|
| ## Dataset Description
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|
|
| FactoryNet unifies multiple industrial operation datasets into a common schema for training anomaly detection and reasoning models. This release includes:
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|
|
| | Dataset | Machine | Task | Episodes | Rows | Faults |
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| |---------|---------|------|----------|------|--------|
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| | **AURSAD** | UR3e (6-DOF cobot) | Screwdriving | 4,094 | 6.2M | 5 types |
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| | **voraus-AD** | Yu-Cobot (6-DOF) | Pick-and-place | 2,122 | 2.3M | 12 types |
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| | **NASA Milling** | CNC (3-axis) | Milling | 167 | 1.5M | Tool wear |
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|
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| **Total: 6,383 episodes, 10M+ rows**
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|
|
| ## FactoryNet Schema
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|
|
| All datasets are converted to a unified schema with causal structure:
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|
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| ```
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| Intent (setpoint) → Action (effort) → Outcome (feedback)
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| ```
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|
|
| ### Core Columns (Tier 1 - Universal)
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| ```python
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| setpoint_pos_0..N # Commanded joint positions (rad) - for robots
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| setpoint_doc # Depth of cut (mm) - for CNC
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| setpoint_feed # Feed rate (mm/rev) - for CNC
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| effort_torque_0..N # Motor torque/current (Nm / A)
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| feedback_pos_0..N # Actual joint positions (rad)
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| timestamp # Seconds since episode start
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| ```
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|
|
| ### Common Columns (Tier 2)
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| ```python
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| setpoint_vel_* # Commanded velocities
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| feedback_vel_* # Actual velocities
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| effort_force_x/y/z # End-effector forces
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| aux_vibration_* # Vibration sensors
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| aux_acoustic_* # Acoustic emission
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| ctx_temp_* # Joint temperatures
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| ```
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|
|
| ## Quick Start
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|
|
| ```python
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| from datasets import load_dataset
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|
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| # Load AURSAD (robot screwdriving)
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| ds = load_dataset("Forgis/factorynet-hackathon", data_dir="aursad")
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| df = ds['train'].to_pandas()
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| print(f"AURSAD: {df['episode_id'].nunique()} episodes")
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|
|
| # Load voraus-AD (robot pick-and-place)
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| ds = load_dataset("Forgis/factorynet-hackathon", data_dir="voraus")
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| df = ds['train'].to_pandas()
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| print(f"voraus-AD: {df['episode_id'].nunique()} episodes")
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|
|
| # Load NASA Milling (CNC tool wear)
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| ds = load_dataset("Forgis/factorynet-hackathon", data_dir="nasa_milling")
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| df = ds['train'].to_pandas()
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| print(f"NASA Milling: {df['episode_id'].nunique()} episodes")
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| ```
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|
|
| ## Minimum Viable Episode (MVE)
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|
|
| Every episode in FactoryNet satisfies the **Minimum Viable Episode** constraint:
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| - ≥1 setpoint signal (commanded intent)
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| - ≥1 effort signal (motor response)
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|
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| This enables causal analysis: if `effort` doesn't follow `setpoint`, something is wrong.
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|
|
| ## Fault Types
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|
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| | Code | Description | Dataset |
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| |------|-------------|---------|
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| | `normal` | Normal operation | All |
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| | `stiff_joint` | Increased joint friction | AURSAD |
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| | `collision` | Contact with obstacle | voraus-AD |
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| | `grip_failure` | Gripper malfunction | voraus-AD |
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| | `missing_part` | Expected part absent | AURSAD |
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| | `tool_wear` | Progressive degradation | NASA Milling |
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|
|
| ## File Structure
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|
|
| ```
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| Forgis/factorynet-hackathon/
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| ├── aursad/
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| │ └── aursad_factorynet.parquet # 1.5 GB - UR3e time series
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| ├── voraus/
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| │ └── voraus_ad_100hz_factorynet.parquet # 461 MB - Yu-Cobot time series
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| ├── nasa_milling/
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| │ ├── nasa_milling_factorynet.parquet # 15 MB - CNC time series
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| │ └── nasa_milling_metadata.json # Episode metadata with wear labels
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| ├── metadata/
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| │ ├── aursad_metadata.json # 78 MB - AURSAD episode metadata
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| │ └── voraus_metadata.json # 53 MB - voraus episode metadata
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| ├── schema.json # FactoryNet schema reference
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| ├── factorynet_loader.py # Easy-load Python utility
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| └── README.md
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| ```
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|
|
| ## Use Cases
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|
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| 1. **Anomaly Detection**: Train classifiers to detect faulty operations
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| 2. **Fault Diagnosis**: Identify which component/joint is failing
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| 3. **Remaining Useful Life**: Predict when tool/component will fail (NASA Milling)
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| 4. **Sim2Real Transfer**: Use real data to calibrate simulators
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| 5. **Robot Q&A**: Answer natural language questions about robot state
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|
|
| ## Citation
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|
|
| If you use this dataset, please cite:
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|
|
| ```bibtex
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| @dataset{factorynet2026,
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| title={FactoryNet: A Unified Dataset for Industrial Robot Anomaly Detection},
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| author={Forgis AI},
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| year={2026},
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| publisher={HuggingFace},
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| url={https://huggingface.co/datasets/Forgis/factorynet-hackathon}
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| }
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| ```
|
|
|
| ## Source Datasets
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|
|
| This dataset unifies and standardizes:
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| - **AURSAD**: [Zenodo](https://zenodo.org/records/4487073) - CC BY 4.0
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| - **voraus-AD**: [GitHub](https://github.com/vorausrobotik/voraus-ad-dataset) - MIT License
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| - **NASA Milling**: [NASA Open Data](https://data.nasa.gov/dataset/milling-wear) - Public Domain
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|
|
| ## License
|
|
|
| CC BY 4.0 - Free to use with attribution.
|
|
|
| ## Contact
|
|
|
| - Forgis AI: hackathon@forgis.com
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| - Physical AI Hackathon: Zurich, Feb 28 - Mar 1, 2026
|
|
|
