###### Abstract
Looking globally at a thermal image, it is easy to notice anomalies by usually comparing adjacent idlers. Therefore, setting a threshold is a trivial task. Similarly, detection of non-rotating idlers (bearing blockage, no contact with the belts). The acoustics provide more complex damage-oriented features (especially spectral) compared to thermography. It allows detecting specific acoustic emissions corresponding to specific elements of the idler and types of damage in various stages of development. For this reason, it is promising from the perspective of both early failure detection but also its prediction [11; 12; 13; 14]. More information about roller damages and belt conveyor issues in general can be found for example in [15; 16; 17; 18]. Given a multitude of different potential issues with the rollers, there are many different diagnostic procedures and algorithms described in the literature. The authors of [19] have presented a review of such methods for the more general problem of fault diagnostics in rotating machinery. In the article, various methods for bearings, but also gears and rotors, have been presented. A similar review was done in [20], although in this case, the authors focused on audio- and vibration-based methods. In [21], the most common bearing fault problem was addressed, and a hybrid method based on autocorrelation analysis was described. To reduce the problem of the interfering vibration signal coming from other sources, the authors proposed the usage of an optimal Morlet wavelet filter. The authors of [22] addressed the tutorial aspects of the bearings' fault diagnostics. To help the engineers learn the concept, three different, very common, approaches to the problem were presented: the autoregressive model, spectral kurtosis, and envelope filter. In [23], a roller state monitoring system implementation on a belt conveyor running in the coal preparation plant was described. The presented roller state diagnostic method based on the stacked sparse encoders and convolutional neural network were able to achieve an accuracy rate of 96.7%. The authors of [24] addressed the problem of the low-speed bearings diagnostics, which can be characterized by the long periods between the defect-related impulses and relatively high level of noise. The proposed autocorrelation-based method was compared to the common envelope spectrum approaches, which turned out to be prone to induce an incorrect conclusion in the low-speed conditions. In [25], an acoustic signal-based method is presented, which utilizes Mel Frequency Cepstrum Coefficients and Gradient Boost Decision Tree for the process of feature extraction and classification. The method achieved over 94.5% accuracy with a very high recall rate of 99.7%. In [26], authors considered the issue of bearing acoustic diagnosis based on a 2D sound field, given the fact that in the single-channel variation, the methods tend to be very sensitive to the sound signal acquisition location. The authors of [27] compared three different methods based on the Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), and Hilbert Huang Transform (HHT) to evaluate their bearing systems fault detection performance. Although many automatic procedures have been developed in practical applications, at the moment, diagnosis of the technical state of the idlers is performed very often by mining staff during current and periodic conveyor inspections. For best results, each element should be checked separately, which takes a considerable amount of time in the case of longer conveyors. Our approach involves an autonomous robot that would constantly monitor the condition of the idler sets with the use of sound processing techniques. Then, during the scheduled maintenance, specialists would only perform basic inspection and repair work on some selected elements without the need to inspect idlers along the entire length of the conveyor.