Advanced Indirect Tire Health Monitoring System Using Transfer Learning for Predictive Maintenance

Tire wear is a critical factor in vehicle safety and operational reliability, with worn-out tires contributing to nearly 30% of highway blowouts and a significant portion of tire-related accidents. Traditional tire wear indicators—such as tread wear bars or mileage-based replacement schedules—lack precision, do not offer real-time feedback, and often fail to prevent hazardous situations caused by uneven or rapid tire degradation. These shortcomings underscore the urgent need for intelligent, data-driven solutions in predictive tire maintenance. While earlier efforts have focused on physical models or basic machine learning algorithms, their limited adaptability and poor generalization across platforms reduce effectiveness in real-world deployments. This paper presents a Machine Learning-based Tire Wear Prediction System, designed for both cloud-based analytics and on-vehicle edge deployment. The system predicts individual tire wear using CAN-based inputs, supported by a transfer learning framework that enables generalization across different tire makes and rim sizes within the same vehicle platform. Rigorous and extensive on-vehicle training and testing ensure robust performance. The solution delivers real-time and highly accurate tire wear estimates along with key features including per-tire wear status, Remaining Useful Life (in % and kilometers), historical wear trend reports, and a tire replacement advisory function. This robust and scalable architecture offers a major advancement in predictive maintenance, equipping OEMs and fleet operators with a reliable tool to enhance safety, extend tire life, and reduce downtime.