## 2 Experiment Methodology
The control system allows one to set and measure the depth of the well as a function of time and torque \(M_{\mathrm{k}}\) (Nm). Drilling speed or the depth of the well is measured indirectly using a magnetostrictive position sensor. The sensor has an encapsulated part containing a transducer and a 50 cm long metal rod. The ring moves freely along the rod. The position of the ring on the rod is evaluated by the transducer and converted into a time delay between two output pulses. The position sensor is fixed on the fixed structure of the stand, and the ring is connected to the moving head, in which the drilling tool is fixed. Thus, simultaneously with the moving drilling tool, the ring also moves along the sensor rod. The drilling stand currently consists of a controlled drill drive, sensors for measuring process variables (ADASH 3900-II), and a Twido control system. This measurement and control system solution was designed to use the stand's existing sensors and control elements. After completing the above-mentioned measuring and control elements, the drilling stand enables experiments to be carried out objectively. The authors of [28] describe the measurement and control system in more detail (see Figure 3). The measured accompanying signal is a response to the operation of the drilling stand. It can be characterized (i.e., with some simplification) as the result of the superposition of a periodic signal derived from the activity of the stand and a periodic signal with a strong stochastic component. It is formed during the operation of individual aggregates and at the interface between the drilling tool and the rock. Research papers [29; 30] present the use of vibration and acoustic signals for determining the geomechanical properties of rocks. This research aimed to find a suitable method that would indirectly determine the state of the aggregates and the process of uncoupling the rock concerning its optimal mode. [PERSON] et al. [31], and [PERSON] and [PERSON] [32] proposed an optimal working mode of the drilling process based on the vibration signal. Such a method is the cluster analysis of the vibration signal [33; 34]. In the cluster analysis of signals in the time domain, it is assumed that the sought-after information is hidden in one of the time characteristics of the signal, such as the signal period, arithmetic mean, dispersion, power, energy, or probability density distribution. Here, a distinction is made whether it is a deterministic signal or a signal with a random component. This was the first partial research task regarding the possibility of process recognition and identification. Simultaneously analyzing the vibration signal in the time domain, its frequency analysis was also performed. As mentioned, signal processing in the frequency domain is based on the idea that any signal can be replaced by a group of simple harmonic signals, each with a different amplitude, frequency, and initial phase. Similarly, the related idea about the origin of the accompanying vibration signal is authentic. Therefore, it is possible to expect two groups of components carrying information about the current state of aggregates during operation. The first group is the harmonic components derived from operating one of the stand's aggregates. The second group is the components caused by working two or three stand aggregates simultaneously. Changes in the frequency spectrum of the signal were investigated and calculated. The authors in [35; 36] processed the measured signal in the frequency domain and estimated the dominant frequencies of the process. In the following research stage, the scientific work focused on calculating the symptom vector from the vibration signal and searching for hidden information about the actual state of the equipment and the drilling process. [PERSON] et al. [37] applied Fourier and wavelet transform analysis methods for vibration signals to extract the dominant frequencies in different layers of the rock mass.