Datasets:

Forgis
/

FactoryBench

license: cc-by-nc-4.0
language:
  - en
task_categories:
  - question-answering
tags:
  - robotics
  - industrial
  - time-series
  - causal-reasoning
  - benchmark
  - machine-understanding
pretty_name: FactoryBench
size_categories:
  - 10K<n<100K

FactoryBench

FactoryBench is a benchmark for evaluating machine-behavior reasoning in time-series models and LLMs over industrial robotic telemetry. Question-answer pairs are organised along the four levels of Pearl's causal hierarchy:

Level	Capability	Example
L1 — State	Identify the operational state from raw signals	"Which fault, if any, is occurring in this episode?"
L2 — Intervention	Predict the effect of an intervention	"How would the joint torques change if the payload were doubled?"
L3 — Counterfactual	Reason about alternative histories	"Would the collision still have occurred if the speed had been 50% lower?"
L4 — Decision	Engineering decision-making (troubleshooting + optimisation)	"Given this anomaly, what is the most likely root cause and remediation?"

The benchmark is grounded in FactoryWave, a dense multivariate telemetry dataset collected from a UR3 collaborative robot (125 Hz) and a KUKA KR10 industrial arm (83 Hz), supplemented with the AURSAD and voraus-AD open-source datasets.

Dataset summary

70,918 Q&A pairs across four causal levels and three splits (train/validation/test).
5 answer formats: single-select MCQ, multi-select MCQ, ranking, tensor/numerical, free-form (judged by an LLM-as-judge voting protocol).
Telemetry from real industrial robots with systematic fault injection (27 atomic mechanisms across pick-and-place, screwing, and peg-in-hole tasks).

Repository layout

FactoryBench/
└── factorybench_qa/                 # Question-answer pairs
    ├── level_1/{train,validation,test}.jsonl
    ├── level_2/{train,validation,test}.jsonl
    ├── level_3/{train,validation,test}.jsonl
    └── level_4/{train,validation,test}.jsonl

Q&A pair counts

Level	Train	Val	Test	Total
L1	12,674	1,338	1,309	15,321
L2	33,311	3,428	3,487	40,226
L3	2,353	265	321	2,939
L4	9,949	1,251	1,232	12,432
Total	58,287	6,282	6,349	70,918

Q&A fields

Each line in factorybench_qa/level_*/*.jsonl is a single Q&A item:

Field	Description
`id`	Unique item identifier
`level`	Causal level (1–4)
`template_id`	Question template the item was generated from
`template_type`	Answer format (`single_choice`, `multi_choice`, `ranking`, `tensor`, `free_form`)
`hides`	Channels/fields hidden from the model in this item
`question`	Natural-language question
`options`	Answer options (for MCQ/ranking templates)
`answer`	Ground-truth answer
`root_cause`	Underlying fault/cause (Level 4 only)
`acceptance_bounds`	Tolerance for numerical answers
`provenance`	Source episode(s) and channels used to derive the item
`context`	Time-series and metadata context exposed to the model

Loading the data

from datasets import load_dataset

# Load a single level/split
ds = load_dataset(
    "Forgis/FactoryBench",
    data_files="factorybench_qa/level_1/test.jsonl",
    split="train",
)

Citation

If you use FactoryBench, please cite the dataset and the two upstream open-source datasets it incorporates (AURSAD and voraus-AD):

@misc{anonymous2026factorybench,
  title  = {FactoryBench: Evaluating Industrial Machine Understanding},
  author = {Anonymous},
  year   = {2026},
  note   = {Submission under double-blind review}
}

@article{leporowski2022aursad,
  title   = {{AURSAD}: Universal Robot Screwdriving Anomaly Detection Dataset},
  author  = {Leporowski, B{\l}a{\.z}ej and Tola, Daniella and Hansen, Christian and Iosifidis, Alexandros},
  journal = {arXiv preprint arXiv:2202.03211},
  year    = {2022}
}

@misc{brockmann2024vorausad,
  title         = {voraus-{AD}: A New Dataset for Anomaly Detection in Robot Applications},
  author        = {Brockmann, Jan Thie{\ss} and Rudolph, Marco and Rosenhahn, Bodo and Wandt, Bastian},
  year          = {2024},
  eprint        = {2311.04153},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO}
}

Intended use & limitations

Intended use. Benchmark evaluation of LLMs and time-series models on structured industrial Q&A reasoning tasks (state, intervention, counterfactual, decision-making).

Limitations. Domain-specific to factory and industrial robotic scenarios; may not generalise to open-domain Q&A. Faults are atomic and drawn from a closed catalogue of 27 physically injected mechanisms — different from compound or gradual real-world faults. The dataset shows a size imbalance between Levels 2 and 3.

Out-of-scope. Not intended for deployment in safety-critical, medical, legal, or financial decision systems without further validation by domain experts.

License

Released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You may share and adapt the dataset for non-commercial purposes with appropriate attribution.