## 1 Introduction
They suggested [PERSON]'s SOM as the most appropriate technique for the classification of AE signatures. [PERSON] et al. [15] used an unsupervised k-means clustering method with a neural network based on a multi-layer perceptron (MLP) and support vector machine (SVM) algorithm for pattern recognition in CFRP rerofitted RC beams. Automatic extraction of the intrinsic characteristics of signals based on deep learning is becoming increasingly popular. Extraction from the time-frequency domain enables the model to learn features, which more comprehensively reflect the intrinsic characteristics in the signal compared to features extracted from the time and/or frequency domain [16]. The clarity of high-fidelity characterization would enable an understanding of the local structural drivers on the damage response of composites [17]. In addition, a single methodology framework, which can be extended not only to various applications, conditions (i.e., industries), and configurations (test materials, data acquisition setups), but even broadly in different engineering domains, can be even more readily adopted [18]. For example, for milling tool wear condition monitoring, a stacked spare autoencoder has been utilized as a feature learning method [19]. The feature value extracted by the traditional method is obtained by manual construction and requires certain professional knowledge [20; 21], while deep learning is used to establish a network model, allowing the network to automatically learn features, avoiding the loss of signals in the time and frequency domains during the manual feature extraction [22]. There are three main types of deep learning network models for sequential pattern recognition: deep belief network (DBN) [23], autoencoder (AE) [24], and convolutional neural network (CNN). A joint neural network model, combining CNN and AE, namely convolutional autoencoder (CAE), enables the extraction of useful higher-level representations from images, bridging the performance gap with DBN and in several cases surpassing it [25]. In CNNs, translation-invariant features enable the model to learn useful descriptors, independent of their location in the image. This property is not entirely desirable for image data where pattern location may carry a significant meaning, such as in time-frequency scalograms. For that reason, fully-connected layers, which induce spatial variance, are employed in the CAE structure at different levels. In this paper, the AE monitoring of damage development with different damage mechanisms in pultruded materials with carbon fiber and glass fiber reinforcement is presented. The aim was to evaluate the performance of a deep convolutional autoencoder (CAE) in providing informative features for the classification and characterization of AE signals obtained from loading fiber epoxy composites. The features, extracted by the CAE model, are denoted as \"deep features\", which means that these features are extracted automatically during the CAE training phase. Therefore, in comparison with \"standard features\" such as peak amplitude, burst signal duration, frequency centroid, etc., deep features do not have physical meaning but are designed to maximize informativeness. The research was designed to investigate various CAE configurations as feature extractors and evaluate the performance by classifying the source material as either carbon fiber epoxy (CFE) or glass fiber epoxy (GFE) composites. Carbon and glass fibers are the most frequently used reinforcements in composites but the description of the classification capabilities with the use of different unsupervised techniques in the literature is scarce, and the application of deep features for this purpose has not been yet reported in the literature. Based on the acquired AE waveforms, deep features were extracted from continuous wavelet transform (CWT) scalograms using a convolutional autoencoder (CAE). Matrix-wise standardization was used in the pre-processing stage.