Multi-Agent Digital Twins for Predictive Maintenance: A Systematic Review
A systematic review published on arXiv examines the integration of Multi-Agent Systems (MAS) and Digital Twins for predictive maintenance in Industry 4.0. The study analyzes over 547 papers from high-impact journals including IEEE Transactions, Nature, Elsevier, and MDPI. It establishes a taxonomy of hybrid architectures, identifies persistent technological bottlenecks, and formulates three open research questions regarding AI deployment on resource-constrained micro-devices, dynamic adaptability, and inter-agent coordination. The review highlights the need for intelligent architectures capable of autonomous decision-making in distributed industrial environments.
Key facts
- Published on arXiv with ID 2607.21873
- Analyzes over 547 papers from IEEE Transactions, Nature, Elsevier, MDPI
- Focuses on predictive maintenance applications
- Identifies three open research questions
- Addresses resource-constrained micro-devices
- Covers hybrid architectures taxonomy
- Highlights need for autonomous decision-making
- Part of Industry 4.0 context
Entities
Institutions
- arXiv
- IEEE
- Nature
- Elsevier
- MDPI