## 6 Discussion and Conclusions  
This work documents the diagnostic analysis carried out on faulty and healthy bearings of a CWRU vibration dataset. Namely, previous research contributions have drawn attention on SK for the enhancement of nonstationarity and on the envelope analysis for signal demodulation. No less research in bearing diagnostics is devoted to feature extraction methods based on data-driven models. However, in this study, the authors placed emphasis on physical phenomena underpinning the resulting experimental data, thus preferring diagnostic methodologies that do not suffer from an excessive detachment from physics-based considerations, which may help data interpretation. Moreover, these techniques are applied to evaluate the capability of damage severity assessments for different diagnostic features. To this aim, the diagnostic content was analyzed both in a numerical and in an experimental signal. The numerical signal was generated by investigating the contact pressure established between the balls and the defected race. Indeed, the contact pressure was supposed to be one of the main sources of excitation in the system dynamic response. Therefore, envelope analysis was applied to extract features and evaluate their damage level sensitivity. The choice of the demodulation band was assisted by FK, which enabled the enhancement of the bearing signal in the vibration data, whereas, in the numerical signal, it contributed to the isolation of the impulsive phenomena. Actually, out an inherent difference between the two signals must be pointed out. In the vibration spectrum, fault harmonics are buried due to the excitation of high-frequency structural resonances and SK acts by bringing to light the bands in which the nonstationary part is hidden. On the contrary, the numerical signal is not influenced by the structural dynamics of the whole test rig, and the peaks do not suffer from damping flattening. Therefore, FK emphasizes impulses that, however, are not completely undetectable in raw spectra. Two health indicators were introduced. They were constructed relying on the assumption that the more severe the damage, the more the signal power strictly relatable to faults is accentuated. In the attempt of isolating this contribution, SK and envelope extraction were applied, resorting to their physical significance. The indicators showed their attitude in identifying faulty bearings with respect to healthy ones. Moreover, the results showed that they may be capable of assessing the damage severity as well. Another compelling aspect is that their physical units are neutral, paving the way for a comparison between the diagnostic contents of the source, which is the pressure signal, and the effect, represented by the vibration data. Indeed, when these signals are evaluated through the mentioned indicators, a correlation is found. Therefore, it is concluded that the energetic indicators may be proposed for damage detection and damage level assessment but, also, as reading keys of a correlation between the vibration and the pressure signal. One of the drawbacks of these indicators is that they precisely point the x_BPFI_ harmonic contents, relying on the detectability of a significant harmonic contribution in these frequencies. However, these analyses were carried out by considering a limited amount of data, both in terms of the fault level and applied loads. Then, future works should include more data in order to validate \begin{table}
\begin{tabular}{c c c} \hline \hline
**Health Indicator** & **r—Correlation Coefficient** & **R2—Coefficient of Determination** \\ \hline HI—damage-related power & 0.89 & 0.79 \\ NPSD & 0.96 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Correlation coefficients. NPSD: normalized power spectral density. and give statistical significance to these considerations. Other fault types should also be investigated, together with the possible prognostic capabilities.