## 4 Conclusions  
The research was focused on the evaluation of the performance of a deep convolutional autoencoder (CAE) in providing informative features for the classification and characterization of AE signals obtained from stressing CFE and GFE fiber epoxy composites. Important conclusions can be summarized as follows:  
1. The analysis of the acquired AE signals based on standard features with decision trees (DTs) and discriminant analysis (DA) classification methods offers accuracy measures of around 80% to classify the signals according to the source specimen, whether it is CFE or GFE.
2. The classification accuracy can be significantly increased if deep features, extracted from the proposed convolutional autoencoder, are used instead of standard features. In this case, the classification accuracy of 91.3% has been obtained by both classifiers. Comparative analysis also indicates good consistency of deep feature selection by both classifiers.
3. The use of both feature sets (standard and deep features) reveals only a slight improvement in accuracy measures compared to the use of deep features only. This proves that deep features already accurately represent the information relevant for the defined classification task.  
Figure 6: The representation of CFG and GFC samples in the space of the two most relevant features (d1 and d2).  
The results of the proposed structural health monitoring approach show that the introduction of deep learning features has a significantly higher potential for the effective characterization of damage mechanisms in fiber-reinforced polymer (FRP) composites in comparison with the use of standard AE features broadly used in the AE testing practice.  
The potential practical applications of the proposed approach are foreseen in the field of AE-based characterization of loaded materials, where improved characterization of structural health monitoring and detection of damage mechanisms are expected due to the application of deep features. Another practical benefit is expected also in possible automation of the feature extraction and feature selection procedures because the CAE method automatically provides the condensed feature set (i.e., deep features) that can be directly applied for the material characterization without additional signal processing.  
Future research directions will focus on the examination of various modified CAE architectures and their hyperparameters, and also on the optimization of the CAE training procedures with respect to robustness, stability, efficiency, and informativeness of the extracted features. Further research is foreseen also in the experimental characterization of different materials and mixed structures to examine the efficiency and applicability of deep features for structural health monitoring of damage modes and early detection of product failures.  
Conceptualization, P.P., M.M. and T.K.; methodology, P.P., M.M. and T.K.; software, P.P., M.M.; validation, P.P., M.M. and T.K.; investigation, M.M. and T.K.; resources, T.K.; data curation, M.M. and T.K.; writing--original draft preparation, P.P., M.M. and T.K.; project administration, R.S. and E.G.; funding acquisition, R.S. and E.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by ARRS--the Slovenian Research Agency, research programs P2-0241 and P2-0270.  
Not applicable.  
The authors declare no conflict of interest.