## 4 Results and Discussion  
The main task of the experimental research was the design of the characteristic vector \(s\) and the determination of clusters during the operation of the drilling equipment aggregates from the vibration signal. First, the experiments were performed on 16 specific symptoms as possible elements of the symptom vector. Then, an analysis was conducted of the values of these symptoms depending on the activity of the aggregates on the horizontal drilling stand. In addition, it was necessary to analyze the differentiability of individual symptoms using the Euclidean metric. The symptoms were always numerically calculated from one realization \(\{x_{i}\}_{i=1}^{n}\) of the signal with the length of the realization of samples and measured with a sampling frequency \(f_{s}=18\) kHz. All proposed symptoms were calculated from \(N=30\) realizations of the vibration signal from the operation of aggregates and normalized according to the Equation (2). It was necessary to look for significantly different symptoms that fundamentally differentiate the operation of aggregates, engine, pump, hydrogenerator, or their combined operation. Another point of view is the effort to exclude common symptoms, unimportant or redundant. The following variables were proposed as state indicators of the aggregates based on laboratory experiments: \(s_{1}\)--mean value (i.e., measure of variability); \(s_{2}\)--variance of the signal time waveform; \(s_{3}\)--skewness; \(s_{4}\)--kurtosis; \(s_{5}\)--peak (i.e., maximum value); \(s_{6}\)--peak2 peak; \(s_{7}\)--rms of the signal; \(s_{8}\)--L\({}_{2}\)x norm of signal time waveform; \(s_{9}\)--signal energy; \(s_{10}\)--signal power; \(s_{11}\)--signal entropy; \(s_{12}\)--L\({}_{2}\)x\(R_{xx}\) autocorrelation norm; \(s_{13}\)--m\({}_{x}\)FFT moment of the signal spectrum; \(s_{14}\)--L\({}_{2}\)xFFT norm of the signal spectrum; \(s_{15}\)--m\({}_{x}\)S\(xx\) moment of the power spectrum of the signal; \(s_{16}\)--L\({}_{2}\)X S\(xx\) norm of the power spectrum of the signal. Due to the large number of results obtained by signal processing, only significant results are presented in this paper. Figure 4 shows individual symptoms \(s_{1}\), \(s_{2}\), \(s_{4}\), \(s_{5}\) of the vibration signal of all aggregates. It is possible to assess the good differentiability of the signals from the display of the symptom waveforms. The biggest recognizable differences are the mean value for the symptom \(s_{1}\) (see Figure 4a). Waveforms of symptoms show strong fluctuations. The highest symptom values \(s_{1}\) have the pump and the hydro generator. On the contrary, the lowest values are during the motor and pump, motor, and hydrogenerator operation. Symptoms \(s_{2}\) and \(s_{5}\) partially have the same waveform (see Figure 4b,c). A greater difference is in the \(s_{4}\)-skewness symptom, which divides the waveforms into two groups. The first group is the aggregates pump, hydrogenerator, and their joint operation. The second group consists of the activity of the other aggregates, while the motor aggregate has the largest values (see Figure 4d). All symptoms were calculated with statistical characteristics. Figure 5 shows the waveform of symptoms \(s_{7}\), \(s_{11}\), \(s_{12}\), and \(s_{16}\). A more detailed description is required (see Figure 5a) with the waveforms of the symptom \(s_{7}\)--rms. It is clear that the symptom \(s_{7}\) strongly divides the aggregates into two groups again. Similarly to \(s_{4}\), \(s_{2}\), and \(s_{5}\), the first group consists of a pump, hydrogenerator, and their combination. Their \(s_{7}\) symptom values are lower than those of the other aggregates. From this point of view, it can be assumed that the symptom \(s_{7}\) is significant for the recognition and classification of vibration signals. Symptoms \(s_{11}\), \(s_{12}\), and \(s_{16}\) are strongly fluctuating in values. The pump has the highest symptom value \(s_{11}\)--entropy. However, the values fluctuate strongly.