## 1 Introduction  
In modern industry, efficient and reliable material handling is a fundamental pillar of productivity and economic efficiency, from raw material extraction to processing plants and logistics centers. Conveyor belts play an essential role in this context, forming large-scale transport systems that enable the smooth and continuous movement of various materials over varying distances, often under challenging conditions. Given their critical function in transporting large volumes, continuous monitoring and diagnosis of conveyor failures and their components are essential for uninterrupted operation [1]. The issue of impact resistance of conveyor belts and thereby the issue of damage to conveyor belts has been addressed by many authors both theoretically and experimentally. [PERSON] [2] observed the impact forces of a falling object in free-fall onto a moving conveyor belt. The aim of the research was to determine the effects of selected parameters (speed of belt movement, impact height, thickness of the damping layer) on the wear of the conveyor belt. [PERSON] [3] states that basic experimental methods for assessing the reliability and quality of rubber-textile conveyor belts include monitoring the impact resistance to belt puncture or splitting, testing belt fatigue, and examining the joins. The development of damage to the conveyor belt carcass on a belt conveyor in an underground mine was studied by [PERSON] et al. [4]. The analysis of the damage progression was conducted not only using a linear regression model but also with a quadratic regression model. The creation of regression models for determining impact forces at the point of impact of the material is used in the papers [5; 6]. [PERSON] et al. [7] uses the finite element method in modeling the deformation conditions of the steel cords in a conveyor belt. [PERSON] et al. [8] tests the mechanical properties of a rubber conveyor belt using its impact load. Internal damage to the conveyor belts was evaluated by computed tomography. [PERSON] et al. [9] use industrial tomography to examine damage to the carcass of a conveyor belt. The forces acting on the conveyor belt during its operation influence its susceptibility to damage, service life, and generally affect its condition. The influence of various factors on the reliable operation of belt conveyors was addressed by the authors in the article [10]. In the articles [11; 12] the contact and tensile forces acting on the conveyor belt were analyzed and evaluated. Using the vibration frequencies of the conveyor, the increase in tensile force was determined in the paper [13]. The causes of dynamic stress in a loaded conveyor belt were examined by the authors in an article [14; 15]. [PERSON] et al. [16] addressed the possibility of using non-contact methods, which among other things use the measurement of the compressive force on the tensioning device of the conveyor. A tension meter-based system helps to solve the issue of monitoring the tension and wear of the conveyor belt in the article [17]. Despite these valuable efforts, all previous studies on conveyor belt impact resistance, wear, and service life often rely on procedures with limited capabilities for accurate prediction or comprehensive damage assessment. This limitation highlights the critical need to augment classical approaches with advanced modeling and analytical methods that can provide a deeper understanding and more precise forecasting of conveyor belt damage. Recognizing this need, incorporating machine learning (ML) into conveyor belt systems represents a promising approach to significantly enhance system reliability and maintenance efficiency. ML plays a pivotal role in data analysis, particularly when the goal is to extract predictions, uncover patterns, or reveal hidden connections from vast and complex datasets that would be challenging for human perception alone.