## 1 Introduction  
Conveyor systems are widely used for horizontal transport in underground mines, where many elements of the conveyor are continuously monitored [1]. [PERSON] et al. [2] proposed a method for predicting the condition and performance evaluation of a conveyor belt based on a dynamic Bayesian network (DBN). The performance of transport systems related to the faults of conveyor belts was examined by [PERSON] et al. [3]. An analysis of conveyor belt (CB) faults was carried out using various evaluation methods, including the visualization of CB damage. During monitoring, it is important to record the failures and causes of CB damage and other problems that arise from the use of conveyor belts [4]. [PERSON] [5] presented a methodology for performing failure analysis with the support of virtual reality. For an online method of detecting the longitudinal rupture of a conveyor, the authors of [6] proposed belt images based on an improved SSR algorithm. A longitudinal rupture analysis was performed in that study to assess the trend of conveyor belt failure prediction [7]. [PERSON] [8] examined the development of a conveyor belt condition monitoring system that actively monitors the conveyor belt. [PERSON] et al. [9] dealt with existing techniques and the latest conveyor belt defect detection methods. [PERSON] et al. [10] present the application of online monitoring of the transport system for the needs of failure analysis, prediction, and evaluation of operating indicators. To improve the efficiency of fault identification, [PERSON] et al. [11] combined an Internet of Things (IoT) platform and a Light Gradient Boosting Machine (LGBM) model to create a fault diagnostics system for a conveyor belt. Currently, many non-destructive testing (NDT) methods have been proposed for belt condition inspection based on machine learning [12]. In [13; 14], the authors proposed a method of detectingconveyor belt damage based on deep learning. [PERSON] et al. [15] proposed a method of detecting conveyor belt damage based on an ADCN (Adaptive Deep Convolutional Network) based on a deep learning algorithm. A deep neural network was used by [PERSON] et al. [16] to design a new method of detecting conveyor belt damage based on CenterNet. User experience has shown that most belt damage is caused by improper operation of the conveyor or its technical condition. [PERSON] et al. [17] presented an overview of CB damage and a classification of their causes using decision trees. For each type of damage, the correct way to prevent and monitor the condition of the belt was indicated. Changes in damage to the CB core can lead to serious defects or even the rupture of the belt. Early detection of damage to the covering layers of a CB can help the user to ensure its maintenance more appropriately. The study in [18] is based on data obtained using the mobile DiagBelt system for non-invasive diagnostics of steel-core conveyor belts. Using non-invasive conveyor belt diagnostics when damage is detected enables a significant reduction in belt replacement costs, as well as evaluation of the belt's usability and the changes in the degree of wear over time. [PERSON] et al. [19] presented an artificial intelligence-based approach for classifying CB damage. [PERSON] and [PERSON] [20] simulated defects in conveyor belts (puncture and longitudinal cuts) and monitored their resistance to these defects. Many studies [21; 22; 23] have focused on investigating the resistance of conveyor belts that have been exposed to impact loads. The results are models that describe the process of the impact of the material on the CB. [PERSON] et al. [24] used video recordings of a moving conveyor belt to detect CB damage. Images taken with a binocular camera were used to detect wear and damage to mining conveyor belts caused by outside materials [25]. [PERSON] et al. [26] proposed a method for detecting CB damage (audio-visual fusion). This method collects, processes, and analyzes CB images and sounds in various operating states.