## 1 Introduction  
Nowadays, the use of data-driven methods is becoming more and more widespread for structural health monitoring (SHM) applications. Deep learning techniques have been applied successfully for SHM because of their capacity to learn representations of massive, monitored data [1, 2, 3, 4, 5, 6, 7, 8]. Convolutional neural networks [9, 10, 11] and recurrent neural networks [12, 13] are among the most popular deep learning techniques used for structural damage monitoring. However, the success of these supervised methods depends on the availability of sufficient labeled data for training. Their applications are mostly limited when applied to complex and large structures because of the difficulty and cost of collecting data corresponding to different health conditions. In fact, these methods can be considered rather idealistic than realistic in some applications. Consequently, more recently, different unsupervised learning methods have been proposed to address SHM issues. Unsupervised learning is based on unlabeled data which may be grouped in a predictable pattern. The most representative of all of these methods are autoencoder models. Deep autoencoders (AEs) are feed-forward multi-layer neural networks which, in the SHM framework, are usually trained with healthy or normal data to reconstruct these same normal data [14, 15, 16, 17, 18, 19]. During an encoding procedure, autoencoders learn the main features of the input data, compressing them by means of lower dimensional projections in a hierarchical way until obtaining their latent representation. Subsequently, the decoder is trained to reconstruct the input data from the features of the latent space. Damage indicators are usually defined from the loss or reconstruction error between the output and the input, since the network is unable to reconstruct input data representative of any structural anomaly. AEs achieve the dimensionality reduction in data features and reduce the pressure of labeled data. Their variants attempt to enhance feature extraction. Traditional approaches of autoencoders [19] involve limitations, since they do not exploit their hierarchical nature, and, additionally, insights into the distribution of available data are lost, since only healthy data are used for training. In this work, one of the main goals was to define damage indicators and analyze the monitored data of the diagnosed structure by exploiting the information of the latent space of an autoencoder model instead of working with the input and output spaces. The latent space captures the underlying structure of the original data in a low-dimensional, compact, and meaningful way. In [20], one approach to defining a health indicator based on the latent reconstruction error is proposed. It was implemented as an extension of the RaPP (reconstruction along projection pathway) method [21] based on the computation of the hidden reconstruction error. In this work, the latent discriminative low-dimensional features of the high-dimensional raw data of monitored vibration signals were exploited and used for analysis. For this, firstly, unlike conventional autoencoders, all the non-labeled original experimental data were used to train an autoencoder model, providing a clustering network in the latent space. A clustering analysis allowed for segmentations in the latent space of the collection of unlabeled data into clusters, according to their similarity. However, the limited availability of real data as well as their unbalanced distribution can make this task difficult, affecting its predictive capability [22; 23]. Multiple representations of datasets involving different classes would be desirable. The sparse availability of data can affect the performance of the autoencoder, decreasing the accuracy of its predictions, especially when addressing detection in those situations that correspond to more minor damage stages. In these cases, a discrete discontinuous distribution with empty spaces is created in the latent space of the autoencoder.