## 4 Results and Discussion
### Sensor Effect and Capability to Detect and Identify the Different Sources  
The sensor can bias the signal by filtering certain frequencies. This is of primary importance when designing an experiment, since the signals obtained from a damage mechanism strongly depend on the type of sensor used [17]. The type of sensor can be varied in the simulation and thus give guidelines to which type of sensor and which location  
Figure 10: Evolution of the Partial Power PP1 [0–125 kHz] vs. amplitude obtained with a perfect sensor for several sources. (**a**) Fiber break and debonding (L = 20 μm to 200 μm), Model A and (**b**) fiber break and debonding, Model A, B, C and D (L = 100 μm). The data were calculated from the epicenter of the source to 30 mm of propagation (distance \(d\)). \(V\) is the velocity of the crack and \(L\) is the debonding length. on the specimen will be optimal. Figure 11 shows the partial power PP1 in the range of [0-125 kHz] as a function of amplitude for several sources with sensor effect. The signals from fiber break had higher amplitudes than those associated with fiber/matrix debonding (Table 3). The comparison between the results associated with the perfect virtual point sensor and those associated with the nano 30 (Figure 11) shows the remaining differences between the amplitude of the fiber break signals and those associated with debonding. Nevertheless, the response of the sensor reduced the frequency centroid compared to the perfect virtual point sensor for the different debonding configurations and for fiber break (Tables 2 and 3). However, we could observe significant changes in the distribution of frequency content (Figure 11b and Table 3). The frequency distribution in the Partial Power PP1 [0-125 kHz] and PP3 [225-450 kHz] domains was different for fiber break and fiber/matrix debonding. The modification was less significant in the range of 125-225 kHz. The signal associated with the fiber contained at least 10% more low frequencies and, on the contrary, the signal associated with debonding had a richer frequency content between 225 and 450 kHz, except for the debonding Model D. The comparison of the values obtained for PP3 and PP1 enabled to discriminate fiber/matrix debonding and fiber break. These results show that in the near field, on the frequency content, it was also possible to differentiate the fiber break from fiber/matrix debonding. Nevertheless, with propagation effects, this difference was blurred beyond 30 mm, making the separation of the mechanisms more complex (Figure 11b). This point is very useful for the classification approaches to increase the robustness of the results. The data of Table 3 show that for the same debonding length (L = 100 \(\upmu\)m) and for Models A and B with instantaneous debonding, the signals had similar values for descriptors (amplitude and frequency). The comparison between Models A and D revealed a significant difference in amplitude and frequency content. This result shows that the debonding condition was a key parameter for acoustic signature. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Fiber Break** & **L = 20 \(\upmu\)m** & **L = 100 \(\upmu\)m** & **L = 100 \(\upmu\)m** & **L = 100 \(\upmu\)m** & **L = 100 \(\upmu\)m** \\  & & **Model A** & **Model A** & **Model B** & **Model C** & **Model D** \\ \hline Frequency centroid (kHz) & 370 & 400 & 405 & 397 & 339 & 327 \\ Peak & 320 & 365 & 365 & 350 & 294 & 205 \\ Frequency (kHz) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Median values of the descriptors for the data collected by a perfect punctual sensor for different sources. The data were collected from the epicenter of the source up to 30 mm away.