## 1 Introduction
The anomaly detection is mostly carried out according to the supervised paradigm, based on observation of relative changes in the inspected part: signals recorded in a current state are compared to a dataset gathered in the pristine state of the structure (i.e., reference or benchmark), serving as the baseline. Deviations, resulting from baseline measurements, may be treated as an indication of damage [13]. Health indicators, damage-imaging approaches, as well as machine learning tools can be used for the damage-related features extraction. In recent decades, significant efforts have been dedicated to the development of UGW-based damage detection algorithms by combining several techniques, including damage index (DI), time of flight, time reversal (TR) technique, and probability-based diagnostic imaging, in order to extract features from the captured signal that can be connected to damage at various states. For instance, the time of flight algorithm relies on the time lag between the incident wave that the sensor first captures and the wave scattered by damage that the same sensor subsequently captures [4; 5; 7; 16]. The time reversal technique consists of identifying the presence of damage in the vicinity of a path between two sensors by verifying the similarity between the reconstructed signal and the original incident signal [17]. Probability-based diagnostic imaging describes a damage event using a color-scale image to represent the likelihood of a damage occurrence at a specific location of the structure [8]. Damage index-based techniques entail calculating the ratio between a structure's initial and diminished impedance capability, which can then be used to determine the severity and location of the damage [18]. Recently, a procedure introduced by [PERSON] et al. [19] utilized the TR method and a Frequencies Compensation Transfer Function (FCTF) approach to reconstruct both Narrow-Band and actual Broad-Band AE signals. A variety of sensor layouts and materialswere used in the study's experimental testing; two different AE sources were also employed: (1) a Numerically Built Broadband (NBB) signal, and (2) a Pencil Lead Break (PLB). Abaqus(r)/CAETM was applied to numerically verify the findings while implementing absorbing boundaries to reduce edge reflections. After that, the suggested method was employed to detect damage in an aluminum flat plate. Furthermore, investigation concerning damage diagnosis methods of components manufactured by FRP composite materials is witnessing increasing attention [11; 20]. [PERSON] et al. [8] used experimental and numerical techniques to inquire about guided wave propagation in the composite winglet structure at various actuation frequencies. They developed a damage detection technique based on their investigation of the guided wave propagation's dispersion property. In the same work, the authors used a composing procedure consisting of Probability Ellipse (PE) and the guided ultrasonic wave propagation technique to investigate the location of a malfunction throughout a composite winglet when subjected to a LVI damage; the sensitivity analysis regarding the location of the actuating PZT was performed, jointly to an analysis on the effects of the load on the PE field value. The presence of a spar stiffener on the dispersion and scattering/reflection phenomena of guided waves were evaluated as well by means of the finite element method. Parallel with a sharp increase in the computational power, modern techniques such as artificial intelligence (AI) have proved their potential. This technology offers several critical benefits that make it an excellent tool for solving a vast majority of complex human problems, making faster decisions, being constantly available, and reducing human error. AI encompasses various methods, including artificial neural networks (ANN), machine learning (ML), and deep learning (DL) algorithms, which are all well suited for SHM and monitoring tasks.