AssetOpsBench：工業資產運營與維護中任務自動化AI代理的基準測試

摘要

工業資產生命週期管理的AI技術旨在自動化複雜的運營流程——如狀態監控、維護規劃和干預調度——以減輕人力負擔並最小化系統停機時間。傳統的AI/ML方法主要孤立地解決這些問題，在更廣泛的運營管道中處理狹窄的任務。相比之下，AI代理和大型語言模型（LLMs）的出現引入了新一代的機遇：實現整個資產生命週期的端到端自動化。本文展望了一個未來，其中AI代理自主管理以往需要專門知識和手動協調的任務。為此，我們引入了AssetOpsBench——一個統一的框架和環境，旨在指導為工業4.0應用量身定制的領域特定代理的開發、編排和評估。我們概述了此類整體系統的關鍵需求，並提供了構建整合感知、推理和控制以應對現實世界工業運營的代理的可操作見解。該軟件可在https://github.com/IBM/AssetOpsBench獲取。

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.

AssetOpsBench：工業資產運營與維護中任務自動化AI代理的基準測試

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

摘要

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