## 4 Results and Discussion
It is different for the hydrogenerator, which has high but relatively constant values of the symptom \(s_{11}\) (see Figure 5b). At \(s_{12}\) and \(s_{16}\), the aggregate-separated motor has the highest values, and a pump and hydrogenerator have the lowest (see  
Figure 4: Waveforms of values of selected symptoms of all aggregates \(s_{1}\), \(s_{2}\), \(s_{4}\), \(s_{5}\): (**a**) symptom \(s_{1}\)—mean values; (**b**) symptom \(s_{2}\)—dispersion values; (**c**) symptom \(s_{4}\)—kurtosis values; (**d**) symptom \(s_{5}\)—maximum values. Figure 5c,d). Symptoms \(s_{7}\), \(s_{11}\), and \(s_{12}\) belong to the specific characteristics of the vibration signal, and symptom \(s_{16}\) is calculated from the frequency characteristic. The presented different waveforms of symptoms provide a good basis for creating clusters, which aim to recognize and classify vibration signals from the operation of the drilling stand aggregates. Among the less suitable symptoms based on their waveforms, it is possible to choose \(s_{3}\), i.e., skewness and \(s_{13}\), i.e., the moment of the signal spectrum (see Figure 6). The values of the symptom \(s_{3}\), i.e., the skewness, oscillate vigorously. Assessing the maximum and minimum values is complicated, and the differences between individual aggregates are negligible (see Figure 6a). At the symptom \(s_{13}\), i.e., the moment of the signal spectrum, two groups of objects are visible (see Figure 6b). The first group consists of a pump, a hydrogenerator, and their joint operation. However, the waveforms overlap so strongly that it is impossible to assess their difference. The second group of objects consists of other aggregates. It is possible to distinguish them partially but not significantly. The important results and indicators are the presentation of the waveform of the symptom values for one aggregate of the drilling stand. Therefore, their plot is essential from the point of view of efficiency of the classification and differentiability of the sought symptoms (see Figures 7-14). Figure 5: Waveforms of values of selected symptoms of all aggregates \(s_{7}\), \(s_{11}\), \(s_{12}\), \(s_{16}\): (**a**) symptom \(s_{7}\)—rms value; (**b**) symptom \(s_{11}\)—entropy values; (**c**) symptom \(s_{12}\)—norm autocorrelation values; (**d**) symptom \(s_{16}\)—norm spectrum power values. Plotting all motor symptoms in Figure 7 gives a partial idea of the possibility of clustering for a single aggregate. For example, from Figure 7b, it can be concluded that \(s_{11}\) has the lowest values, other symptoms for the motor are not fully pronounced, and their values are relatively high, such as \(s_{7}\) and \(s_{8}\) (see Figure 7a). Figure 8 shows all the symptoms for the pump. From Figure 8a, it can be concluded that the symptoms have relatively low values except for \(s_{1}\) and \(s_{3}\). A similar situation is  
Figure 8: Pump as the aggregate: (**a**) symptom values \(s_{1}\)–\(s_{8}\); (**b**) symptom values \(s_{9}\)–\(s_{16}\). Figure 6: Waveforms of values of selected symptoms of all aggregates \(s_{3}\), \(s_{13}\): (**a**) symptom \(s_{3}\)–skewness values; (**b**) symptom \(s_{13}\)—moment spectrum values. Figure 7: Motor as the aggregate: (**a**) symptom values \(s_{1}\)–\(s_{8}\); (**b**) symptom values \(s_{9}\)–\(s_{16}\). seen in Figure 8b where \(s_{11}\), \(s_{12}\), and \(s_{15}\) have higher values. The other symptoms for the pump are not significant. Figure 9a,b show all the symptoms for the hydrogenerator. A similar symptom analysis applies to the hydrogenerator as to the pump. From Figure 10a,b of the operation of the motor and pump, it is possible to draw the partial conclusion that the symptoms are well differentiated and have a constant waveform. The exceptions are \(s_{1}\) and \(s_{3}\). From Figure 11a,b, which show the operation of the motor and hydrogenerator, it is possible to derive a similar partial result as that for the motor and pump operation.