Prediction of Impact Damage and Critical Operating Conditions of Conveyor Belts Based on CT Diagnostics and Machine Learning

The article investigates damage in textile-reinforced rubber conveyor belts caused by impact loading. The study aims to evaluate how impact conditions and belt structural properties affect severe damage formation and to develop predictive models for identifying critical operating conditions. Damage assessment was performed using visual inspection and computed tomography (CT), with CT serving as a reference method due to its ability to detect internal defects in the load-bearing carcass. CT identified more severe damage cases than visual inspection, confirming its higher sensitivity. Experimental tests were carried out with impact heights between 0.8 and 2.6 m and impact weights from 50 to 100 kg. The results showed that impact energy is the dominant factor influencing damage formation, as higher impact heights and weights significantly increased the probability of severe damage. Belt structural characteristics also affected damage resistance, especially the thickness of the top cover, which reduced the risk of failure. To predict severe damage, Logistic Regression, Random Forest, and XGBoost models were applied, all achieving excellent performance (AUC > 0.95). Logistic Regression (AUC = 0.994) additionally enabled the estimation of damage probability and the identification of critical impact conditions. The proposed approach supports safer operating limits, risk assessment, and predictive maintenance in conveyor systems.