AI-TYRE: AI-Based Early Anomaly Detection in Tire Testing

•Aim

The project aims to develop an AI-based system for early anomaly detection in tyre testing environments. By leveraging real-time sensor data (temperature, pressure, load, speed, vibration, noise, thermal imaging, image processing), the system will detect anomalies that precede tire failures during the tests and provide early warnings. Secondly, it will show the exact point of the failure on the tyre. 

•Innovation

The project introduces a novel integration of generative AI and time-series anomaly detection models (LSTM, Autoencoder) into high-speed tire testing environments. It combines real-time data acquisition with explainable AI to enhance safety and reliability.

•Technical Approach

The system will be trained on historical and real-time test data. It will be deployed in a test laboratory environment with sensor integration and real-time monitoring. The AI models will be validated through physical testing and simulation.