## 2 Related Work  
### Literature Review  
The area of diagnosis, monitoring, and prediction of damage to conveyor belts has undergone significant development in recent years, primarily thanks to progress in machine learning (ML) methods, advanced sensor technologies, and innovative analytical approaches. Traditionally, it relies on conventional methods, such as visual inspection often supplemented by sensors [19], ultrasonic measurements, or 2D laser systems for measuring belt thickness [20]. For example, [PERSON] et al. [21] successfully applied 2D lasers and statistical methods for the detection of longitudinal cracks. Although these techniques provided a detailed overview of the material's condition, their main limitations lay in insufficient automation and low time efficiency, which made them less suitable for large-scale and continuous industrial applications. Similarly, metrotomography offers exceptional precision in the analysis of the internal structure of belts [22]; however, its use is limited by laboratory conditions and high demands on resources. These inherent limitations of traditional approaches underscored the urgent need for the development of more sophisticated and automated diagnostic solutions for industry. Despite the shift towards advanced methods, classical statistical predictive models, such as regression and linear discriminant analysis (LDA) [23, 24], still retain their place in certain applications, especially where model transparency and rapid application without extensive datasets are critical. These methods, including logistic regression and various classification models, have been successfully applied in the analysis of defects in rubber composites [25] and rubber-textile conveyor belts [26]. Complex evaluation of conveyor belt im-pact resistance was also realized using canonical correlation analysis [27]. However, despite their contribution to identifying correlations in data, their sensitivity to nonlinearities and strong assumptions about data structure often limit their ability to effectively solve complex and dynamic problems in modern industrial systems. This challenge led to the need for a transition to approaches that can better process nonlinear and extensive data, which corresponds with the development of information technologies and the advent of ma-chine learning-based solutions [28]. These persistent challenges led to the massive development of modern diagnostic approaches based on artificial neural networks (ANN) and deep learning (DL). These methodologies have demonstrated an excellent ability to process high-dimensional and complex data, significantly automating and increasing the precision of damage detection. For example, a study [29] effectively demonstrated the detection of conveyor belt damage using a two-layer neural network, pointing to the potential of ANN in automating defect identification and contributing to predictive maintenance. Similarly, [PERSON] et al. [30] focused on detecting steel cord faults using statistical analysis and neural networks. The expansion of the spectrum of ML models, including decision trees, Random Forest, convolutional neural networks (CNN), and generative adversarial networks (GAN), subsequently enabled efficient processing of extensive operational data and offers the possibility of continuous, contactless monitoring [31; 32; 33; 34; 35; 36]. An example of practical deployment is the faster and lighter detection method based on improved YOLOv5 [37] for foreign objects in coal mine belt conveyors, which excels in rapid and accurate localization of even small defects in real-time. In the broader context of ML application in industry, [PERSON] et al. [18] demonstrated the capabilities of ANN in predicting energy consumption, while [PERSON] et al. [38] proposed a comprehensive framework for the creation and validation of predictive models in mining belt systems.