Experimental study of heavy-duty tire load estimations based on internal surface feature point distance monitoring

Current methods for estimating heavy-duty tire loads rely on indirect approaches such as cameras, radar, and accelerometers to obtain tire deformation information. Empirical formulas are then used to fit and estimate tire loads. However, these methods have low estimation accuracy, poor real-time performance, and high susceptibility to environmental conditions. When a tire is under load, it deforms, causing the distances between internal surface points to change predictably. Therefore, we propose the estimation of tire vertical loads by analyzing the distances between characteristic internal surface points.

We designed a monitoring system consisting of flexible strain sensors, data acquisition and transmission equipment, and upper-computer control software to monitor the deformation patterns of internal surface points under load. We then installed axial, circumferential, and lateral distance sensors in the tire. Using vertical load, tire pressure, and speed as variables, we conducted experiments on a drum test bench to measure the changes in internal surface characteristic point distances under load. We analyzed the periodic deformation patterns of different internal points during tire movement and identified the peak circumferential contact stretch at the tire crown and the noncontact stretch at the tire sidewall as characteristic points for estimating vertical load. We also used correlation analysis to examine the effects of speed and pressure on these points. Based on these two characteristic points, we developed empirical, semiempirical, and machine-learning models for estimating the vertical load of heavy-duty tires.

The machine-learning models demonstrated excellent performance. Among them, the XGBoost model performed the best, with a coefficient of determination (R²) of 0.9856, a root mean square error (RMSE) of only 181 kgf, and a mean absolute error (MAE) of 128 kgf, displaying high accuracy and stability. The Random Forest model achieved an R² of 0.9705, an RMSE of 382 kgf, and an MAE of 294 kgf, also exhibiting strong regression performance. The neural network model showed moderate error metrics. In comparison, traditional models performed less reliably. The semiempirical model had an R² of 0.9288 and moderate error metrics. The empirical model for the crown circumferential region significantly outperformed that for the sidewall region, highlighting the differences in mechanical properties and deformation patterns between these areas. The sidewall region is more challenging to monitor and estimate.

The verification results demonstrate that the proposed method for estimating the vertical load of heavy-duty tires based on internal surface feature point distance monitoring achieves high accuracy. This approach not only provides a new method for tire load estimation but also offers a technical solution for predictive maintenance of heavy-duty tires. Additionally, the proposed detection method and load estimation model have important reference value for load monitoring of tires in heavy machinery and can be applied to a broad range of tire-based transportation equipment.