Multi-Agent System-driven Digital Twins for predictive maintenance: architectures, technologies and open research challenges

• URL: http://arxiv.org/abs/2607.21873v1
• Date: Fri, 24 Jul 2026 00:09:53 GMT
• Title: Multi-Agent System-driven Digital Twins for predictive maintenance: architectures, technologies and open research challenges
• Abstract summary: Digital twins have emerged as a foundational technology within the context of Industry 4.0.<n>This systematic review explores the intersection between Multi-Agent Systems and Digital Twins.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Integrating Heterogeneous Digital Twins in Federated Ecosystems [0.8582085540322195]
  textitFederated Digital Twin Ecosystems have emerged as a promising direction.<n>This paper proposes the textitFederation Node Manager, a modular integration mechanism that connects local DTs to a federated environment.<n>We present a conceptual design and a prototype implementation, and demonstrate their feasibility in the smart mobility domain for emergency response scenarios.
  arXiv  Detail & Related papers  (2026-06-22T03:04:11Z)
• Digital Twins & ZeroConf AI: Structuring Automated Intelligent Pipelines for Industrial Applications [3.534869097377701]
  This work proposes a modular and interoperable solution that enables seamless AI pipeline integration into Cyber-Physical Systems.<n>We introduce the concept of Zero configuration (ZeroConf) AI pipelines, where DTs orchestrate data management and intelligent augmentation.<n>The approach is demonstrated in a MicroFactory scenario, showing support for concurrent ML models and dynamic data processing.
  arXiv  Detail & Related papers  (2026-02-04T10:11:06Z)
• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey [59.3507264893654]
  Issue resolution is a complex Software Engineering task integral to real-world development.<n> benchmarks like SWE-bench revealed this task as profoundly difficult for large language models.<n>This paper presents a systematic survey of this emerging domain.
  arXiv  Detail & Related papers  (2026-01-15T18:55:03Z)
• Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models [95.9909582708447]
  Digital twins, as precise digital representations of physical systems, have evolved from passive simulation tools into intelligent and autonomous entities.<n>This paper presents a unified four-stage framework that characterizes AI integration across the digital twin lifecycle.
  arXiv  Detail & Related papers  (2026-01-04T01:17:09Z)
• A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems [53.37728204835912]
  Most existing AI systems rely on manually crafted configurations that remain static after deployment.<n>Recent research has explored agent evolution techniques that aim to automatically enhance agent systems based on interaction data and environmental feedback.<n>This survey aims to provide researchers and practitioners with a systematic understanding of self-evolving AI agents.
  arXiv  Detail & Related papers  (2025-08-10T16:07:32Z)
• From MAS to MARS: Coordination Failures and Reasoning Trade-offs in Hierarchical Multi-Agent Robotic Systems within a Healthcare Scenario [3.5262044630932254]
  Multi-agent robotic systems (MARS) build upon multi-agent systems by integrating physical and task-related constraints.<n>Despite the availability of advanced multi-agent frameworks, their real-world deployment on robots remains limited.
  arXiv  Detail & Related papers  (2025-08-06T17:54:10Z)
• Deep Research Agents: A Systematic Examination And Roadmap [109.53237992384872]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)
• Distinguishing Autonomous AI Agents from Collaborative Agentic Systems: A Comprehensive Framework for Understanding Modern Intelligent Architectures [0.0]
  The emergence of large language models has catalyzed two distinct yet interconnected paradigms in artificial intelligence: standalone AI Agents and collaborative Agentic AI ecosystems.<n>This study establishes a definitive framework for distinguishing these architectures through systematic analysis of their operational principles, structural compositions, and deployment methodologies.
  arXiv  Detail & Related papers  (2025-06-02T08:52:23Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.