## 2 Related Work
### Literature Review
These works collectively emphasize critical factors for successful industrial deployment, such as model robustness against data noise, adaptation to changing operating conditions, and the need for iterative model tuning. The synergy of advanced sensor systems (e.g., industrial cameras, 2D lasers) and deep learning algorithms (e.g., CNN, GAN, hybrid approaches) [39; 40; 41; 42] is particularly beneficial for more demanding applications, such as contactless detection of cracks and surface defects. [PERSON] et al. [42], for example, successfully applied machine vision for crack detection in a demanding and contaminated industrial environment, thus confirming the high reliability of this technology. The effectiveness of combining UHF RFID (Ultra High Frequency Radio Frequency Identification) with neural networks for fast and accurate detection of belt faults was also demonstrated by [41]. The latest research continues to push the boundaries of defect detection, with deep learning proving to be a reliable tool for damage detection in mining conveyors [43; 44], and [PERSON] et al. [45] also contributed to the progress. The integration of multiple visual models to increase safety was presented by the authors in [46]. Although these solutions significantly contributed to reducing inspection error rates, especially in demanding industrial operations, a key and persistent challenge remains the need for extensive, high-quality annotated training datasets and continuous model calibration for adaptation to changing operating conditions. To solve the problem of limited datasets for rare damage, methods like meta-learning and few-shot learning [47] are emerging, with the meta-learning model achieving significantly better accuracy with a small number of training examples compared to conventional methods. Most ML and artificial intelligence (AI) applications today indeed aim for increased detection accuracy even in environments with disruptive factors (contamination, vibrations); however, a limit remains the need for extensive, high-quality annotated datasets, validation across belt types, and adaptability to changing operating conditions [38; 48]. In addition to damage detection, machine learning is increasingly used for operational optimization and risk assessment. [PERSON] [49], for example, illustrated how genetic algorithms in combination with neural networks can estimate the necessary power for conveyors, which is crucial for energy efficiency. [PERSON] et al. [28] presented a comprehensive framework for risk assessment in mining conveyor systems, emphasizing the importance of a systematic approach to safety and reliability. In the area of load analysis and signal classification, deep learning has proven to be extremely effective. [PERSON] et al. [50] investigated deep learning models for precise classification of rubber belt loading, which is crucial for the development of intelligent monitoring systems with real-time response and optimization capabilities. In summary, and in the context of persistent challenges, the transition from traditional to advanced ML/AI methodologies, comprehensive review frameworks, and robust sen sory systems has fundamentally strengthened the possibilities of continuous monitoring, predictive maintenance, effective lifespan analysis, and optimization of conveyor belts across the full range of operating conditions [51].