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language:
- en
pretty_name: AssetOpsBench
configs:
- config_name: scenarios
data_files:
- split: train
path: data/scenarios/all_utterance.jsonl
default: true
- config_name: compressor
data_files:
- split: train
path: data/asset/compressor_utterance.jsonl
- config_name: hydrolic_pump
data_files:
- split: train
path: data/asset/hydrolicpump_utterance.jsonl
- config_name: rule_logic
data_files:
- split: train
path: data/task/rule_monitoring_scenarios.jsonl
- config_name: failure_mode_sensor_mapping
data_files:
- split: train
path: data/task/failure_mapping_senarios.jsonl
- config_name: prognostics_and_health_management
data_files:
- split: train
path: data/task/phm_utterance.jsonl
license: apache-2.0
task_categories:
- question-answering
- time-series-forecasting
tags:
- Industry
- PHM
- Predictive-Maintenance
- Asset-Management
- tool-learning
- task-automation
- LLM
- Multi-Agent
size_categories:
- n<1K
---
# 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},
}
```
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