Machine learning-driven intelligent tire wear detection system

Abstract

Traditional methods detect wear by interpreting mathematical models and tire characteristics; however, these methods struggle to accurately reflect the actual rolling condition of the tire. In this study, we propose a machine learning-based tire wear detection module that can provide accurate results under tire test rig conditions. To develop this module, we designed three key components: integrated acceleration and PVDF sensors within the tire to capture vibration and deformation data; signal preprocessing algorithms to highlight multi-source signal differences under varying wear conditions; and deep learning algorithms to achieve precise tire wear grade identification. Experimental results demonstrate that, under different tire pressures, loads, speeds, and wear levels, the system can accurately identify tire wear grades with 99.99% accuracy by combining data from both sensors.