| --- |
| 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}, |
| } |
| ``` |
|
|
