Structural Health Monitoring (SHM) aims to monitor in real-time the health state of engineering structures. For thin structures, Lamb Waves (LW) are very efficient for SHM purposes. A bonded piezoelectric transducer (PZT) emits LW in the structure in the form of a short tone burst. This initial wave packet (IWP) is then propagating in the structure and interacts with its boundaries and discontinuities, such as the presence of damage, generating additional waves packets. In this sense, both the geometry itself and the presence of damage can produce complex behaviors in the measured signal from sensors, which makes the extraction of features that will be used later to evaluate damage detection very complicated in complex scenarios. To solve this issue, here an innovative Deep Learning technique called Rank Reduction Autoencoder (RRAE) [1] is considered. The RRAE consists on an autoencoder whose latent space is restricted to be expressed as a low-rank SVD approximation, capturing in this sense only the most important features of the studied signals, by keeping the advantage of learning complex behaviours by means of a nonlinear approximation. The novelty proposed in this work consists of adding an additional restriction in the latent space of the RRAE so that its content is as representative as possible to produce damage detection. This is achieved by means of a MLP (Multi-layer perceptron) that takes as inputs the latent space and delivers as prediction the location of damage. Therefore, the training of the RRAE plus the extraction of important features by means of a MLP all together allows to obtain at convergence a powerful tool for damage detection in the SHM field. To illustrate the proposed technique, it is applied for the detection of damage on a thin plate.