---
title: AssetOpsBench
canonical_url: "https://www.modelscope.cn/datasets/ibm-research/AssetOpsBench"
md_url: "https://www.modelscope.cn/datasets/ibm-research/AssetOpsBench.md"
repository: ibm-research/AssetOpsBench
last_updated: 2026-05-29
license: apache-2.0
storage_size: "9.6 MB"
downloads: 390
stars: 0
---

# AssetOpsBench

> AssetOpsBench - ibm-research 在 ModelScope 开源的数据集。AssetOpsBench is a specialized benchmark designed for evaluating Large Language Models (LLMs) and Multi-Agent systems in industrial operations. It focuses on the intersection of sensor data interpretation,…

ibm-research/AssetOpsBench 是 ModelScope 魔搭社区上的数据集，存储大小 9.6 MB，采用 apache-2.0 许可。

- **Repository**: ibm-research/AssetOpsBench
- **License**: apache-2.0
- **Storage size**: 9.6 MB
- **Downloads**: 390
- **Stars**: 0
- **Last updated**: 2026-05-29

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

---

# AssetOpsBench

**AssetOpsBench** is a specialized benchmark designed for evaluating Large Language Models (LLMs) and Multi-Agent systems in industrial operations. It focuses on the intersection of sensor data interpretation, maintenance logic, and **Prognostics and Health Management (PHM)**.

The benchmark enables researchers to test how effectively AI agents can manage complex industrial assets, such as compressors and hydraulic pumps, by applying rule-based logic and diagnostic reasoning.

## 📂 Dataset Structure

The dataset is divided into several configurations to allow for granular testing. Users can load data for a specific **Asset** type or **Task** type.

### Baseline Configurations (Data Center Infrastructure)
This core set focuses on critical cooling systems within data center environments:
* **Asset Coverage**: Includes data from 4 Chillers and 2 Air-Handling Units (AHUs).
* **Lifecycle Tasks**: Benchmarks a model's ability to perform Anomaly Detection, Automated Sensor Mapping, and Work Order Generation.

### Asset Configurations
Focus on hardware-specific sensor patterns and operational contexts:
* **Compressor:** Data related to industrial air and gas compressors.
* **Hydrolic Pump:** Data focusing on fluid power systems and pressure diagnostics.

### Task Configurations
Focus on the reasoning and automation capabilities:
* **PHM (Prognostics and Health Management):** Tasks centered on predicting Remaining Useful Life (RUL) and assessing State of Health (SoH).
* **Rule Logic:** Evaluating the model's ability to trigger actions based on predefined industrial maintenance thresholds and logic.

## 🚀 Getting Started

You can load the default scenario or a specific configuration using the Hugging Face `datasets` library.

### Loading the Default Scenarios

```python
from datasets import load_dataset

dataset = load_dataset("ibm-research/AssetOpsBench", "scenarios")
```

### Loading a Specific Asset (e.g., Compressor)
```python
from datasets import load_dataset

dataset = load_dataset("ibm-research/AssetOpsBench", "compressor")
```

## Cite this Dataset
If you use our dataset in your paper, please cite our dataset by
```
@misc{patel2025assetopsbenchbenchmarkingaiagents,
      title={AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance}, 
      author={Dhaval Patel and Shuxin Lin and James Rayfield and Nianjun Zhou and Roman Vaculin and Natalia Martinez and Fearghal O'donncha and Jayant Kalagnanam},
      year={2025},
      eprint={2506.03828},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2506.03828}, 
}
```