| Issue |
MATEC Web Conf.
Volume 415, 2025
International Colloquium on Mechanical and Civil Engineering (ICMCE’2025)
|
|
|---|---|---|
| Article Number | 03005 | |
| Number of page(s) | 11 | |
| Section | Artificial Intelligence and Optimization | |
| DOI | https://doi.org/10.1051/matecconf/202541503005 | |
| Published online | 27 October 2025 | |
AI-Driven Predictive Maintenance for Intelligent Tires: A Real-Time Digital Twin Framework
1 Research Laboratory in Science and Engineering FST Fez, Morocco
2 Department of Mathematics, Computer Science, and Physics, Rockford University, Illinois, USA
* Corresponding author: imane.elouadrhiri@usmba.ac.ma
The current trend in the technology of intelligent tire is revolutionizing vehicle performance and safety by integrating advanced sensing methodologies, Digital Twin, and Artificial intelligence. This paper introduces an approach of Predictive Maintenance framework that combines machine learning, sensor fusion, and cloud-based analytics enabling a realtime tire health monitoring, performing a self-repair if needed to maintain an optimized state of the tire. An AI driven diagnostic, Digital Twin virtual simulation integrated an advanced physical system is implemented. The model proposed in this research is detecting early faults, reducing maintenance costs and enhancing road safety. The framework goes beyond traditional tire monitoring systems by integrating adaptive algorithms and cutting-edge data processing techniques, enabling a continuous and dynamic condition assessment of the tire. This approach represents A key step towards a robust, scalable tire management system aligning with the trend of Industry 4.0 standards and the future of autonomous driving and mobility.
Key words: Intelligent Tires / Digital Twin / Predictive Maintenance / Industry 4.0 / Artificial Intelligence / Reinforcement Learning / Page layout
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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