AssetOpsBench：工业资产运维任务自动化AI代理基准测试平台

AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance

June 4, 2025

作者: Dhaval Patel, Shuxin Lin, James Rayfield, Nianjun Zhou, Roman Vaculin, Natalia Martinez, Fearghal O'donncha, Jayant Kalagnanam

cs.AI

摘要

面向工业资产全生命周期管理的AI技术，旨在自动化复杂的运营工作流程——如状态监测、维护规划及干预调度——以减轻人力负担并最小化系统停机时间。传统的AI/ML方法主要孤立地解决这些问题，仅在更广泛的运营流程中处理特定任务。相比之下，AI代理与大型语言模型（LLMs）的出现带来了新一代机遇：实现贯穿整个资产生命周期的端到端自动化。本文展望了一个未来，其中AI代理自主管理以往需依赖专门知识与手动协调的任务。为此，我们推出了AssetOpsBench——一个统一框架与环境，旨在指导为工业4.0应用量身定制的领域特定代理的开发、编排与评估。我们概述了此类整体系统的关键需求，并提供了构建集成感知、推理与控制能力以应对现实工业运营的代理的可操作见解。该软件可在https://github.com/IBM/AssetOpsBench获取。

English

AI for Industrial Asset Lifecycle Management aims to automate complex operational workflows -- such as condition monitoring, maintenance planning, and intervention scheduling -- to reduce human workload and minimize system downtime. Traditional AI/ML approaches have primarily tackled these problems in isolation, solving narrow tasks within the broader operational pipeline. In contrast, the emergence of AI agents and large language models (LLMs) introduces a next-generation opportunity: enabling end-to-end automation across the entire asset lifecycle. This paper envisions a future where AI agents autonomously manage tasks that previously required distinct expertise and manual coordination. To this end, we introduce AssetOpsBench -- a unified framework and environment designed to guide the development, orchestration, and evaluation of domain-specific agents tailored for Industry 4.0 applications. We outline the key requirements for such holistic systems and provide actionable insights into building agents that integrate perception, reasoning, and control for real-world industrial operations. The software is available at https://github.com/IBM/AssetOpsBench.