## 3 Results
### The Detection of PCRRf Patterns
#### 3.2.2 The Extraction and the Analysis of the PCRRf2 Patterns  
Similar to Figure 15, the variation of the VIAEPf (as VIAEPf2a) over time during a first experiment (without DUM) at 1500 rpm (theoretical synchronous speed) is shown in Figure 24 (also with 2,000,000-\(h\) samples, and \(\Delta t=10\) us). Zooming in on region A shows (as an example) the local character of the instantaneous active electrical power variation. Figure 24: The variation of the VIAEPf2a over time during a first test (without DUM) with the motor running with no load (at 1500 rpm theoretical synchronous speed) used to extract the PCRRf2a pattern by FAS. Figure 23: Graphical descriptions of two periods: 1—the analytical PCRR1a pattern; 2—the analytical PCRR1b pattern. Using the FASS procedure--based on Equation (3)--it was possible to extract a first PCRRf2 pattern (as PCRRf2a, with \(T_{p2a}=0.0400189077/73371\) s, \(n_{2a}=4002\) and \(m_{2a}=460\)) described with two periods, with curve 1 in Figure 25. In the same figure, curve 2 shows a PCRRf2b pattern (from a second identical sequence, VIAEPf2b, sampled after 200 s, with the motor running continuously after the first experiment), and curve 3 shows a PCRRf2c pattern (from a third identical sequence, VIAEPf2c, sampled after 400 s, with the motor running continuously after the first experiment). Curve 4 shows the overlaid patterns of PCSL2a, b, and c, also with two periods. There is a logical assumption that PCRRf2a, b and c patterns are strictly correlated with PCSL2a, b and c patterns (PCRRf2a, b and c start and end on a minimum of PCSL2a, b and c). The FASS method of extracting both types of patterns means that they no longer start strictly on the PCSL2a, b and c minima (due to averaging with a high \(m\) value and local variation of \(T_{p2a,b,c}\) periods). Thus, after the initial selection of the sample position for \(k=1\) (from Equation (3)) on a minimum of the \(u_{SL}[s\cdot\Delta t]\) signal and obtaining the patterns, for each pair of patterns (e.g., PCRRf2a and PCSL2a), the value of \(k\) is slightly adjusted appropriately until the PCSL2a starts strictly on its minimum (or more simply, until the \(n\)th sample of PCSL2a is exactly its minimum). In this way, we are sure that the PCSLa, b and c patterns overlap as much as possible (as Figure 25 clearly shows), and therefore, we expect to have the correct overlap of the PCRRf2a, b and c patterns (for comparison between). As Figure 25 clearly shows, the PCRRf2a, b and c patterns (found under similar experimental conditions) overlap quite well, having quite similar shapes and amplitudes, which once again indicates that this type of pattern is an important indicator within the IAEPf, which is useful for describing the state of the motor running with no load. It is interesting to remark that the average peak-to-peak amplitude of these patterns is smaller than the average peak-to-peak amplitude of the patterns PCRRf1a and b (Figure 16). There is a hypothesis here: probably the dynamic system of the rotor and the rotating magnetic field now acts as an attenuator; the variable mechanical phenomena (at a higher instantaneous speed now than before) are reflected with reduced amplitudes in the IAEPf. The practical usefulness of this PCRRf2 pattern can be illustrated experimentally by showing how it changes with the introduction of different no-load running conditions for the motor rotor, e.g., by adding the DUM to the rotor [10], at different angular positions (a topic already discussed before). According to Figure 3, the DUM was placed on the jaw coupling hub mounted on the rotor (at a distance of 35 cm from the axis) at 60 degrees before the angular marker 2, relative to the direction of rotation.