## 4 Discussion
Those components whose \(T_{c}\) period is described by \(z\) samples and whose \(h/z\) ratio is strictly an integer are completely eliminated (the transmissibility for these components having \(1/T_{c}\) frequencies and satisfying this condition is zero), e.g., the 3 rd harmonic within the pattern PCRRF1a, as shown in Table 2. All other components whose \(T_{c}\) period is described by \(z\) samples and which do not satisfy the above condition (with respect to the \(h/z\) ratio) have a reduced amplitude at the filter output (greatly reduced when \(z<h\)). Additionally, some components are affected with a shift of phase of 180 degrees at the time origin. At the present stage of research, it is practically impossible to determine the description of the sinusoidal components completely removed by BMAF. For sinusoidal components whose amplitudes have been artificially diminished, an attempt has been made to restore the correct amplitude values (by multiplying with the inverse of the transmissibility, \(1/Tr(f)\), as a function of frequency). For the components whose phases at the time origin have been changed, their correct phases have been restored by adding 180 degrees. Although the approach is correct in principle, the results of this restoration of correct amplitude and phase values at the time origin are relatively good, as shown in Figure 23 (which shows the results of restoring two different PCRRF1 patterns but determined under the same experimental conditions). If this approach had been correct, the two reworked patterns should have matched better. There is a very simple reason for this partial mismatch: the identification and description of the sinusoidal components (amplitudes, frequencies and phases at the origin of time) of the extended patterns were not performed very accurately. We hope that the use of a better identification method (as a future challenge) will overcome this shortcoming. Despite this relative inconvenience, it is clear that PCRRF patterns are valuable in experimental research, especially for early fault detection. As a summary of this work, a brief description of the signal processing is given using the flowchart shown in Figure 30. The flowchart also contains some other information (e.g., the equations used, the numerical results and some examples in figures). It is reasonable to assume that some further studies will confirm the capability and possibly the limitations of these proposed solutions for monitoring the condition of the motor with deliberately introduced mechanical and electrical faults or with different characteristics (e.g., rotor cage shape and structure, number of pole pairs, magnetic field distribution, etc.). It is important to note that some motor faults can be characterised by different variations of the IAEPf over time on each of the three phases of the electrical supply (due to the asymmetry of their currents). The test bench shown in Figure 2 can be extended with two more ICT transformers and two more IVT transformers (placed on phases B and C), whose output signals can be accessed via four other available oscilloscope input channels. In this way [3; 17], it is possible to extract the PCRRf patterns corresponding to the power carried on each of the three phases. These faults can be highlighted by the different appearance of the three PCRRf patterns. A future approach will be specifically dedicated to the extraction of PCRRf patterns by FASS within the IAEPf supplied by three-phase AC-AC converters used to motorise the spindle of CNC machines, including those using voltages and currents that are not sinusoidal. Obviously, the subject of motor condition monitoring and diagnostics topic should be reconsidered as being linked to that of the electrical power system [46; 47]. This study can be further applied to the identification of patterns within many other experimental signals in stationary regimes (e.g., vibration, surface roughness [48], voltage, current, run out, etc.