## 1 Introduction  
Structural Health Monitoring (SHM) is a process that implements techniques to detect, monitor, and make decisions about the assessment of the condition of structures using methods that report and prevent damage, applying maintenance management systems to the lifetime extensions of structures [1, 2, 3]. Some of the changes in the structures are due to natural disasters, environmental conditions, deterioration, and aging during the life cycle of the structure service, causing impacts on the health condition of the structure [1]. The data-driven methods approach has been employed in SHM, especially for damage detection, involving four phases: detection (determine the occurrence of damage event), location (identify the location of damage), typification (estimate severity and type of damage), and prediction (prognosis of damage) [4]. These methods require data collection using different types of data acquisition systems for dynamic measurements of the structure with wired or wireless sensors [5; 6]. The selection of the physical variables depends on the structure and the analysis required for the detection of abnormal behavior or changes in the structural characteristics (the first two phases of damage). Vibration signals are one of the most commonly used variables for the detection, location, and assessment of damage in several types of infrastructure, such as offshore wind turbines, towers, bridges, buildings, and tunnels [2; 7; 8; 9]. These signals provide information about the dynamical properties of the structure, such as changes in the modal parameters that could be a potential indicator of damage, specifically variations in the natural frequencies, mode shapes, and damping ratios [10; 11; 12]. One of the techniques used to obtain these parameters is Operational Modal Analysis (OMA), where the identification of the parameters is performed via the measurement response of the structure without measured force excitation. Measured force acquisition is required in Experimental Modal Analysis (EMA), which could be demanding, especially in large structures for health monitoring purposes [13; 14]. OMA experiments use a random excitation, such as ambient environment or self-generated excitation (white noise, harmonic or transient excitation) to extract modal parameters [15; 16]. However, the installation and monitoring of multiple sensors to measure vibration data is considered a problem for the analysis of these data. Specifically, the concatenation of feature vectors obtained from the signals of each sensor increases the dimensionality of the resulting feature vector. All of these data are loaded into the dataset, which is often imbalanced due to the majority of observations corresponding to the normal operation state of the structure and a small percentage corresponding to the damage state of the structure. In some cases, these damage scenarios are the result of simulation structures using numerical models, such as Finite Element Models (FEM) and Boundary Element Method (BEM) [8; 17], or scale model structures submitted under damage states. This need is mainly due to the challenges involved in accurately assessing real damage to a structure, considering its impact, cost, and the safety of civil infrastructure [12; 18]. The implementation of signal processing methods for accurate diagnosis and damage detection in complex mechanical systems and civil structures is essential to ensure operational safety and facilitate effective maintenance. Specifically, for SHM applications using vibration signals, identifying anomalies, assessing environmental influences, or tracking operational changes present significant challenges. These include handling vibration signals that not follow a Gaussian distribution, the necessity for historical data covering both healthy and damaged conditions, and the extraction of robust features to accurately diagnose system state [13; 19; 20; 21].