# Analysis of Uncertainty in Conveyor Belt Condition Assessment Using Time-Based Indicators
## 1 Introduction  
Belt conveyor systems are an indispensable component of mining infrastructure, particularly in open-pit mines, where they enable continuous and automated transport of large volumes of overburden and coal [1]. Their reliability and operational availability are critical for production continuity; any downtime resulting from conveyor belt failures leads to significant economic losses [1, 2]. The belt itself is the central element of these transport systems. Its design--especially the inclusion of steel-cord reinforcement in the core--determines its mechanical strength, resistance to dynamic loading, and durability under harsh operating conditions [3, 4]. Numerous studies have shown that belt wear encompasses not only surface degradation, such as cover abrasion, but also internal damage: cracks, bends, crushes, and corrosion of the steel cords [5, 6, 7]. Internal core defects are particularly hazardous, as they remain hidden from external inspection for extended periods and are often detected only after failure has occurred. Consequently, the development of diagnostic techniques capable of identifying thesehidden defects has become a major focus in maintenance engineering. Contemporary non-invasive inspection methods include magnetic systems (e.g., DiagBell) [3; 8], X-ray imaging [9; 10], and advanced vision-based systems for detecting surface damage [5; 11; 12]. Parallel to these methods, machine-learning algorithms are being developed to support belt condition classification and prediction, ranging from classical support vector machines [11] and regression models [13] to advanced deep learning architectures such as generative adversarial networks [12]. In comparison to alternative methods, such as X-ray imaging [9; 10] or vision-based surface inspection systems [5; 11; 12], the DiagBelt+ system provides continuous online scanning of the belt core, detecting internal defects without interrupting conveyor operation. Unlike classical magnetic systems, DiagBelt+ offers higher spatial resolution and automated defect quantification, making it suitable for high-throughput diagnostics in large-scale open-pit mines. From the perspective of fracture mechanics and conveyor system design, studies on belt resistance to dynamic impact (punctures), where lumps of material fall from height and strike the belt at discharge points, are essential [14; 15; 16]. Parameters such as mass, shape, sharp edges on the falling objects, drop height, and belt handling at loading points, including belt support, influence stress distribution and potential damage. Drop-test experiments and computed tomography have demonstrated that heavy impacts can cause steel-cord fractures and cuts, delamination of rubber covers, and micro-damage, subsequently developing into long-term core defects [15; 16]. Equally significant are the properties of the transported material--its hardness, shape, granulation, and content of fine mineral fractions. Studies [2; 17; 18] have shown that abrasive, aggressive materials--particularly overburden--cause significantly greater cover wear and internal damage than milder loads, such as lignite. Laboratory tests using dry-sand/rubber-wheel apparatus [17] and field analyses [2] have unequivocally demonstrated that higher abrasivity can reduce belt lifespan significantly. Another vital research area is identifying defect causes and systematizing them using quality-analysis tools such as the Ishikawa (fishbone) diagram [19; 20]. This diagram organizes factors affecting belt damage into six main categories: (1) material characteristics; (2) belt construction; (3) operational parameters; (4) condition of discharge points; (5) maintenance and diagnostic quality; and (6) environmental factors. Causal analysis facilitates the identification of \"bottlenecks\" in operations and planning of corrective and preventive actions. To structure knowledge of potential degradation causes of steel-cord conveyor belts, Figure 1 presents an Ishikawa diagram.