发表机构
USMBA（巴马科阿卜杜勒·瓦希德·托马西大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
探讨多智能体系统与数字孪生交叉点，聚焦资源受限环境下预测性维护。通过分析547篇论文建立分类法、识别瓶颈，提出在微控制器部署AI、多节点协调及分层编排数字孪生等问题，揭示现有系统未满足工业5.0要求。
AI 中文摘要
数字孪生已成为工业4.0背景下的一项基础技术，为物理系统的实时虚拟表示提供了一种范式。然而，管理其日益增长的复杂性，特别是在分布式工业环境中，需要具备自主决策、动态适应性和智能体间协调能力的智能架构。本系统综述探讨了多智能体系统与数字孪生的交叉点，尤其关注资源受限环境中的预测性维护应用。通过对发表在高影响力期刊（IEEE Transactions、Nature、Elsevier、MDPI）上的547篇论文进行批判性分析，我们建立了现有混合架构的分类法，识别出持续存在的技术瓶颈，并提出了三个开放研究问题：（i）在资源受限的微控制器上部署人工智能；（ii）通过轻量级通信协议进行分布式多节点协调；（iii）将数字孪生进行分层编排以实现集成剩余寿命估计和可解释人工智能的智能工厂控制。分析结果表明尽管取得了重大进展，但现有系统均未提供同时满足工业5.0要求的集成嵌入式-分布式分层解决方案。
英文摘要
Digital twins have emerged as a foundational technology within the context of Industry 4.0, offering a paradigm for the real-time virtual representation of physical systems. However, managing their growing complexity, particularly in distributed industrial environments, requires intelligent architectures capable of autonomous decision-making, dynamic adaptability, and inter-agent coordination. This systematic review explores the intersection between Multi-Agent Systems and Digital Twins, with a particular focus on predictive maintenance applications in resource-constrained contexts. Through a critical analysis of over 547 papers published in high-impact journals (IEEE Transactions, Nature, Elsevier, MDPI), we establish a taxonomy of existing hybrid architectures, identify persistent technological bottlenecks, and formulate three open research questions concerning: (i) the deployment of artificial intelligence on resource-constrained microcontrollers, (ii) distributed multi-node coordination via lightweight communication protocols, and (iii) the hierarchical orchestration of Digital Twins toward smart factory control integrating residual life estimation and explainable Artificial Intelligence. The results of this analysis reveal that, despite significant progress, no existing system offers an integrated embedded-distributed hierarchical solution that simultaneously meets the requirements of Industry 5.0.
Comments33 pages, 5 figures, 4 tables, paper submitted to Internet of Things Journal(Elsevier)
