Condition monitoring of intelligent tires utilizing 1D CNN and vibration measurement

Tire maintenance plays a crucial role in vehicle performance, with the tire being identified as the most important factor. In this study, we introduce an intelligent tire system equipped with composite sensors to enhance driving safety and vehicle management. Analysis of actual traffic accident data reveals that approximately 10% of accidents are attributed to tire-related issues, emphasizing the significance of tire maintenance. However, our investigation suggests that while conventional time and frequency domain techniques are available for fault detection in intelligent tires, they tend to exhibit slightly lower performance compared to those utilizing artificial intelligence. To address this limitation, we propose a deep learning-based diagnosis method. By attaching a 3-axis accelerometer sensor to the tire tread and simulating various failure modes, including Belt/ Bead separation, comprehensive data for analysis were collected. We develop a novel approach using multi-scale feature fusion with adaptive weight calculation using 1-D convolution principles, which significantly improves fault detection accuracy. Experimental results demonstrate the effectiveness of our proposed method, achieving a 100% F1 Score in the classification of Tire Separation faults. Visualization using Uniform Manifold Approximation and Projection (UMAP) further confirms distinct clustering for each fault state. Overall, our study offers valuable insights into tire fault diagnosis and management, contributing to enhanced vehicle safety and performance.