## 1 Introduction
Moreover, signal propagation suffers drastically from boundary reflections, which complicates the identification of the modes. [PERSON] et al. [38] extended the analyses to a dissipative medium including a relaxation function. In the case of viscoelastic plates, [PERSON] et al. [14] developed a model for AE wave propagation and predicted the displacement based on the ray theory. [PERSON] et al. [23] investigated the dynamic behaviour due to a single fiber break using finite element methods and carried out parametric studies with different laws for the matrix effect (elastic, a non-linear elastic and an elastic-plastic model) and different fiber positions. A dynamic finite element method (DFEM) has been developed for prediction AE waveforms in the far field plates [24; 26]. [PERSON] et al. [31] modelled, by a finite element method, AE sources in a composite material for different damage mechanisms, in addition to the signal propagation and the different elements of the acquisition chain. They compared the experimental values of the descriptors with the theoretical values generated by the damage mechanism in order to validate the labelling of the signals. [PERSON] et al. [33] have implemented a deterministic modular AE model using analytical formulation for AE sources, finite element methods for the wave propagation and reception by finite sized transducers. For the signal detection, some analytical approaches considered the sensors transfer function explicitly. Some authors [39; 40; 41; 42; 43] have proposed a finite element approach to include the piezoelectric sensor in a modelling environment in order to take into account the interface between the sensor and the material. In this work, we focus on identifying AE features due to fiber break using the Finite Element Method. We perform experiments and simulations on a model composite consisting of an epoxy matrix with a single carbon fiber. In the experimental part, we use different types of sensors both in the experiments and in the simulations in order to understand and to quantify the sensor effect on the signal waveform. Indeed, the problem that has received the least attention is that of the sensor effects. Moreover, this model is used to analyse and decompose simulated AE \"primitive \"signals without a sensor near the source. We underline the interest in using the results obtained with the modelling work, in order to increase the robustness of the supervised classification of AE signals. Indeed, a supervised classification is based on the creation of a library of labelled data. This approach is based on the assumption that the feature measurements and the identified classes for the library are error-free. However, one cannot exclude that the library may contain uncertainty. The performance of the classifier depends on both the number of the available training signals as well as the specific values of the signals. In this context, merging the simulated and the experimental data enables to enlarge the library. The present paper is organized as follows: The experimental procedure and the acoustic emission analysis are first detailed in Section 2. The model is described in Section 3. The validation of the numerical method is presented in Section 4. With the validated FEM, the out-of-plane and the in-plane velocities corresponding to fiber breaks are calculated. At first, our approach is to study the numerical AE signals without any sensor effect (with a virtual perfect contact point sensor), then adding the sensor effect and compare the results. In the parameter study, the effects of the sensor, the thickness of the specimen, the position of the fiber are investigated in order to generate artificial data that can be merged with experimental data in a training set. The results of the parametric study are discussed in Section 5. As an additional point, we have performed 2D Fourier Transform-based time/frequency analyses in order to improve our understanding of the frequency content of AE signals and the effects of both sensor and propagation on it.