Toward smart tire management: A data-driven unsupervised learning approach

This study presents a data-driven approach for smart tire management through the application of unsupervised machine learning techniques. Using a real-world dataset comprised with synchronized records collected at 1 Hz from a sensor-equipped fleet vehicle, the research investigates how the use of clustering algorithms – K-Means and BIRCH with Agglomerative Clustering – can be employed to identify distinct operational stages in the usage cycle of tires. A comprehensive descriptive analysis was first conducted to understand the behavior and correlations among pressure, temperature, and speed data. The clustering analysis, applied both globally and by individual tire positions, revealed that the optimal number of clusters can vary depending on the tire’s location. The findings highlight the importance of position-aware tire analytics and support the development of intelligent tire management systems capable of optimizing performance, enhancing safety, and extending tire lifespan.