## 1 Introduction
As part of the research, the accompanying vibration signals were investigated as an integrating source of information about the state of the rotary rock separation process and the drilling equipment's current state. By examining the measured accompanying signals, it was shown that periodic mechanical vibrations and stochastic mechanical shocks occur at the indentor-rock interface during the drilling process, corresponding to elementary mechanical processes of cutting and chipping the material. These vibrations and shocks subsequently cause a corresponding noise. The researchers in [12; 13] performed an experimental study of the drilling process on selected rocks to determine whether vibration signals can help classify drill bit wear. Based on this theory, a scientific assumption was made that the mechanical vibration signal and the accompanying signal contain information about the conditions and the state of the rock disintegration process and the technical condition of the drilling equipment, which could be used for identification and process classification. In [14; 15], monitoring and evaluation of sounds generated as unwanted by-products at the bit-rock dividing line were proposed to predict the type of rock being drilled. The idea of using vibrations makes it possible to apply the analysis of vibration signals in the field of technical diagnostics or condition monitoring. In addition to the measurement result, the goal is to provide an evaluation and, in the narrower case, a diagnosis of the object's state (see Figure 1). Further research showed that near the optimal drilling operating mode, the accompanying measured signal has typical identifiable and classifiable characteristics. A recent study [16] proposed a technique to improve the feature extraction capability before using the PCA method for feature selection. The authors of the manuscript deal with the scientific topic of effective control of the drilling process based on the classification of vibration signals from the aggregates of the drilling stand. [PERSON] et al. [17] investigated vibrations and their effect on the aggregates of a horizontal drilling stand. Vibration signals from the working modes of drilling and individual aggregates were continuously measured. Subsequently, a symptom vector was extracted for each vibration signal. On this basis, clusters characterizing the operation of the drill stand unit and the regime of the rock being disintegrated were recognized and classified using the cluster method. [PERSON] et al. [18], [PERSON] and [PERSON] [19] applied a machine learning approach to classify technical components in the manufacturing equipment. A cluster expertly obtained offline for the aggregate of the drilling stand was shown to represent an effective mode of operation. The proposed approach to solving the task of classifying the condition of the equipment and controlling the drilling process allows for artificial intelligence methods to be used, such as clustered analysis. Its application to mining operations is justified by the complexity and stochasticity of the drilling equipment and the drilling process (see Figure 2). In various research works [20; 21], the authors described the classification system of drilling stand aggregates in more detail. Then, using a mathematical formalism, they explained the principle of a classifier based on cluster analysis. They implemented selected artificial intelligence methods. The results indicate a strong potential for solving scientific and technical tasks. The preliminary results of the scientific research were presented based on an investigation of the dependencies between the properties of the accompanying signals and clusters created by the activity of the aggregates. [PERSON] et al. [22] applied cluster analysis in seismic contexts, where it can help improve the prediction accuracy of machine learning methods for earthquakes in mines. This paper presents essential new knowledge from the original research on the classification of vibration signals from the drilling stand aggregates' operation.