## 1 Introduction  
Conveyor belts play a crucial role in material transportation systems due to their ability to operate continuously and reliably. They are widely used in various industries, such as mining, logistics, and manufacturing, thanks to their versatility, flexibility, and efficiency. Conveyor belts can transport large quantities of different materials under various working conditions over long distances in a continuous manner, significantly improving process efficiency. Additionally, they offer lower operational costs and greater environmental benefits compared to other transportation methods, such as road or rail transport. Over the years, extensive laboratory research has led to the development of advanced solutions for conveyor belt construction, operation, applications, monitoring, and control methods [1, 2]. Conveyor belts can be used in production halls, mines, and open spaces, serving both single devices and extensive transportation systems. The diagnostics of conveyor belts primarily involves inspecting the belt itself as a material carrier, along with drive components, motors, gear mechanisms, and other structural elements. Conveyor belts are one of the most challenging components to diagnose, particularly in cases of damage caused by physical, chemical, or biological factors. Typical belt damages include mechanical issues such as cuts, punctures, and dents, as well as chemical, thermal, or biological deterioration. Early detection of such issues enables preventive measures, thereby extending the lifespan of conveyors [3,4]. Diagnostic methods encompass a wide range of non-destructive testing techniques using Industry 4.0 technologies. Real-time monitoring, supported by artificial intelligence tools and deep learning models, has a direct impact on the efficiency and safety of conveyor belt operations [5,6]. Ultrasonic sensors and vibroacoustic analysis help detect cracks and other conveyor belt damages, as well as issues with bearings, drums, and other drive components [7, 8, 9]. Vision and thermographic systems utilize cameras and image analysis algorithms to monitor the belt's condition, identifying visible surface damage such as cracks and abrasions while also detecting drive-related issues [10, 11, 12, 13]. The literature studies indicate that the development of conveyor belt monitoring systems is dynamic. The projected increase in expenditures on such technologies highlights their growing importance in optimizing production processes. The integration of non-destructive testing methods with machine learning algorithms and advanced data acquisition techniques contributes to improved reliability and reduced operational costs of conveyor systems. However, measurement data collected under industrial conditions are often affected by various factors that negatively impact data quality [14]. Existing solutions for detecting and identifying damage in conveyor belts, while effective in many applications, often face specific challenges. These include the precise localization of damage in fabric-based belts and the frequent requirement for expensive and specialized equipment, such as belts with embedded sensors, probes, or additional diagnostic devices. Furthermore, many of these methods necessitate planned conveyor shutdowns for inspection, leading to potential downtime. Their effectiveness can also be limited to detecting specific damage types, depending on their orientation and the diagnostic technology employed. In this context, the strain gauge system presents an attractive complementary or alternative solution, distinguished by its low implementation and operational costs. Its key advantage lies in its ability to continuously measure loads and stresses in real time, without the need for structural intervention in the belt or stopping the conveyor. This enables the early detection of subtle changes in the transport system's operation, which can indicate potential issues such as belt deformation, pulley misalignment, or uneven wear.