## 1 Introduction  
Belt conveyors are among the most important means of transport of bulk material in a variety of industries, from mining to food processing. They gained their position because they combined high efficiency that allowed the transport of large capacities in a short time with cost effectiveness. Report [1] indicates that, due to the importance of conveyors to the operation of mines, a failure in conveyor operation due to malfunction, repairs or replacement can cause losses up to EUR 1 mln per hour. Nevertheless, like any other machinery, the smooth functioning of conveyors relies on regular maintenance. Neglecting this aspect can result in severe outcomes. Analysis of accident data in the report of the French Ministry for an Ecological and Solidarity Transition [2] shows an increasing number of reported belt conveyor fire accidents, indicating that they were mainly (67% of cases) a result of equipment defects, with a leading cause of them in 43% being the inadequate frequency and quality of preventive maintenance. In [3], the authors give an example of such catastrophic consequences of the lack of proper maintenance--the belt conveyor fire in a Brazilian mining company. Therefore, regular inspection and proper maintenance are expected not only to reduce the costs of unexpected malfunctions but also to prevent more serious accidents. Currently, the primary method of maintenance involves periodic inspection carried out by human workers, who check elements along the conveyors which can span for many kilometers in a typical industrial plant. Such inspections have low efficiency and are costly, not to mention the additional hazard to the personnel operating in the vicinity of the running machinery, collectively leading to rare check schedules. Consequently, a number of research and development works are underway to automate belt conveyor inspection. It is expected that introducing automated inspection in place of that performed by humans will be beneficial in many aspects. This includes a reduction in both the time and costs of unexpected downtime due to an increased frequency of the inspections. Moreover, it will improve human resource allocation as fewer personnel will be necessary to be allocated for searching faulty elements and enable more focus on actual repair tasks. Lastly, the shift to automated inspection is expected to reduce the safety risk by relocating personnel from in-mine presence to remote operation. Up until now, the most advances were achieved in automating the inspection of main active components that can be continuously monitored using stationary equipment, such as the driving systems and belts. These individual units can be equipped with dedicated monitoring systems. However, monitoring the idlers remains a challenge. The idlers form the support carrying the belt, and typically consist of a frame and a number of idler rolls (cf. Figure 1). With idlers positioned every several meters along the conveyor, the number of idler rolls may exceed thousands per kilometer. Damage to an idler roll can lead to severe issues, such as belt trips, overheating, and even fires. Various types of idler roller damage and their potential consequences for belt integrity are presented in [6]. Therefore, it is crucial to detect issues with idlers before they deteriorate into failures. Due to the fact that the deterioration of idler rolls is a source of vibrations, excessive friction, noise, and heat [7], various solutions were developed for inspection and pre-failure detection in idler rolls. The technologies used include the analysis of the audio signal from the microphone array processed with convolutional neural networks (CNNs) [8], SVM, or various NNs [9], infrared (IR) or RGB images [4; 10], load and speed sensors, and combinations of these [11]. These solutions allow for automation of the idler roll state analysis. However, there remains an issue of delivery of the measurement unit to each individual idler roll.