## 3 Results and Discussion
### Influence of the Choice of Sensor  
The cumulative AE energy recorded by both sensors, \(\upmu\)80 and PicoHF during tensile tests, is shown in Figure 5 for the vinylester composite. The global behaviour is equivalent in terms of the energy recorded. Nevertheless, we can notice that the \(\upmu\)80 sensors have better signal detection than PicoHF sensors, especially during the first part of the test. In addition to a different sensitivity, the choice of sensor plays an important role in the characteristics of the recorded signals. Figure 6a,b show the whole data population recorded at both sensors in the plane amplitude/frequency barycentre for the signals recorded during a tensile test on glass fibres/PA6.6 polyamide composites and for the glass fibre/vinylester composites. It is clear that the sensor noticeably distorts the AE signals. Table 2 summarizes the mean value and the lower and upper limits for several AE descriptors. In order to  
Figure 4: Evolution of the recorded acoustic energy, the frequency centroid and the relative modulus versus strain for a tensile test on CMC at room temperature. (The input signal is generated with a frequency in the range of 150 kHz and 950 kHz, amplitude 5 volts and rise time 20 \(\upmu\)s, actuator \(\upmu\)80 sensor).  
check the possibility to structure these data, an unsupervised classification is conducted. The majorities of the descriptors have an exponential distribution, which often makes them incomparable with other descriptors such as amplitude or mean frequency, which have Gaussian distributions. It may be useful to apply a natural logarithm to these \"exponential\" descriptors, so that their distributions can be approximated using a Gaussian law. The dendrogram allows the choice of the same descriptors (In(Rise time), Duration, amplitude, In(energy), FP (frequency peak) and FC (frequency centroid)). According to the DB (Davies and Bouldin) and SI (Silhouette) indices [2; 13], the classification lead to a solution with four classes. Figure 7a,b shows the representation of the classes in the plane frequency centroid/amplitude. Figure 8 represents the radar chart for the normalized median values of several classes. The same colour is attributed to the equivalent classes obtained with the two kinds of sensors. The differences in the segmentation is visible. The sensor bandwidth and its sensitivity have a significant influence on the degree of class separation and class characteristics. Moreover, it is difficult to establish a link between the characteristics of the class identified with the two kinds of sensors. The aim of compensating the effect of sensor seems to be difficult. The majority class (Red class) recorded by the sensor u80 is reduced by 90% with the sensor picoHF. The class with the highest rise time (blue class), second majority class with the sensor u80 is also drastically reduced with the PicoHF sensor (reduced by 80%). For the most energetic classes (black and green classes), the same number of signals are recorded but the repartition is different. The Black class is the majority energetic class with the u80 sensor instead of the green class for the picoHF. Even if it is possible to identify an equivalent structuration of data, the correlation with the mechanisms of damage is more complicate with the use of different sensors.