---
title: FailureSensorIQ
canonical_url: "https://www.modelscope.cn/datasets/ibm-research/FailureSensorIQ"
md_url: "https://www.modelscope.cn/datasets/ibm-research/FailureSensorIQ.md"
repository: ibm-research/FailureSensorIQ
last_updated: 2026-02-25
license: apache-2.0
storage_size: "11 MB"
downloads: 167
stars: 0
---

# FailureSensorIQ

> FailureSensorIQ - ibm-research 在 ModelScope 开源的数据集。FailureSensorIQ Dataset

ibm-research/FailureSensorIQ 是 ModelScope 魔搭社区上的数据集，存储大小 11 MB，采用 apache-2.0 许可。

- **Repository**: ibm-research/FailureSensorIQ
- **License**: apache-2.0
- **Storage size**: 11 MB
- **Downloads**: 167
- **Stars**: 0
- **Last updated**: 2026-02-25

Source: https://www.modelscope.cn/datasets/ibm-research/FailureSensorIQ

---

# FailureSensorIQ Dataset

FailureSensorIQ is a Multi-Choice QA (MCQA) dataset that explores the relationships between sensors and failure modes for 10 industrial assets.

|[**Github**](https://github.com/IBM/FailureSensorIQ) | [**🏆Leaderboard**](https://huggingface.co/spaces/cc4718/FailureSensorIQ) | [**📖Paper**](https://arxiv.org/abs/2506.03278) |

## Dataset Summary
FailureSensorIQ is a Multi-Choice QA (MCQA) dataset that explores the relationships between sensors and failure modes for 10 industrial assets. By only leveraging the information found in ISO documents, we developed a data generation pipeline that creates questions in two types: (i) FailureMode2Sensor and (ii) Sensor2FailureMode. Additionally, we designed questions in a selection (select the relevant ones) vs. elimination (eliminate the irrelevant ones) format. 

FailureSensorIQ dataset consists of 8,296 questions across 10 assets, with 2,667 single-true multi-choice questions and 5,629 multi-true multi-choice questions. The following is the list of assets with count within 2,667 single-true multi-choice questions:

| Asset                       | Count |
|-----------------------------|-------|
| Electric Motor              | 234   |
| Steam Turbine               | 171   |
| Aero Gas Turbine            | 336   |
| Industrial Gas Turbine      | 240   |
| Pump                        | 152   |
| Compressor                  | 220   |
| Reciprocating IC Engine     | 336   |
| Electric Generator          | 234   |
| Fan                         | 200   |
| Power Transformer           | 544   |

Please find more statistics of the dataset and the dataset construction process from our [Github](https://github.com/IBM/FailureSensorIQ) and [Paper](https://arxiv.org/abs/2506.03278).

## Load the Dataset
To load 2,667 single-true multi-choice QA,

```python
from datasets import load_dataset

# Login using e.g. `huggingface-cli login` to access this dataset
ds = load_dataset("ibm-research/FailureSensorIQ", "single_true_multi_choice_qa")
```

To load  5,629 multi-true multi-choice QA,
```python
from datasets import load_dataset

# Login using e.g. `huggingface-cli login` to access this dataset
ds = load_dataset("ibm-research/FailureSensorIQ", "multi_true_multi_choice_qa")
```

## Leaderboard
We have benchmarked both open-source LLMs and frontier LLMs on FailureSensorIQ. Furthermore, we have conducted thoroughout Perturbation–Uncertainty–Complexity analysis for deeper insights of each LLMs. For the most updated leaderboard, please refer to our [Leaderboard](https://huggingface.co/spaces/cc4718/FailureSensorIQ). You can submit the evaluation there. 

If you want to reproduce our results, please check out our [Github](https://github.com/IBM/FailureSensorIQ) for the evaluation scripts. We documented all the steps you should take to run the evaluation.

## Cite this Dataset
If you use our dataset in your paper, please cite our dataset by
```
@misc{constantinides2025failuresensoriqmultichoiceqadataset,
      title={FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes}, 
      author={Christodoulos Constantinides and Dhaval Patel and Shuxin Lin and Claudio Guerrero and Sunil Dagajirao Patil and Jayant Kalagnanam},
      year={2025},
      eprint={2506.03278},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.03278}, 
}
```