Their proper performance is essential to avoid unplanned stops and catastrophic failures that can lead to significant economic losses and safety risks. Bearing failure can cause major breakdowns or damage to the machinery where it is installed; in the most serious cases, it can also cause catastrophic failures. Therefore, predicting possible malfunctions in advance to correct them prevents unplanned downtime and, in turn, reduces production losses and associated costs. In this regard, the statistical data provided in [4] show that 41% of failures in electric motors are due to bearings, while 37% occur in the stator, 10% in the rotor and 12% due to other various causes. This is where machine learning techniques, signal processing and spectrum analysis come into play. These tools are capable of processing sensor data in real time and helping to identify failure patterns before serious operational problems arise. From this angle, the deep learning-based approach is particularly relevant in this context, because these models can autonomously learn from large datasets and are, in turn, competent in recognizing complex patterns in bearing signals if they are, indeed, appropriately modified and trained on a regular basis. This fact allows them to detect anomalies and incipient faults with increasing accuracy. Moreover, the optimization methodology plays a crucial role in fault diagnosis, as it involves the appropriate configuration of machine learning algorithms, the selection of relevant features and the continuous improvement of the model as more data are collected. Bearing failure investigation is an area extensively studied by the global scientific community and a crucial issue in condition monitoring of rotating machines [5; 6; 7; 8]. [PERSON] and [PERSON] developed an approach based on finite element analysis of vibrations to identify single or multiple failures under the influence of an unbalanced force in different elements of the bearing structure, using time and frequency domain properties [9]. [PERSON] and [PERSON] carried out a detailed investigation into the nature of vibration signals at different phases of impacts of faulty rolling elements, using these data to calculate the size of the defects [10]. [PERSON] and [PERSON] performed a detailed analysis of the signals from Case Western Reserve University (CWRU) using their reference methodology, which is based on discrete separation preprocessing and the use of envelope spectrum to detect faults [11]. In the field of artificial intelligence [12], numerous articles have been published suggesting various techniques to train models for bearing diagnosis. In the field of machine learning, [PERSON] et al. presented an approach based on Support Vector Machines and the Markov model to anticipate the degradation process of bearings [13]. [PERSON] et al. obtained features based on acoustic emission analysis using the Hilbert-Huang transform and used them to train a k-Nearest Neighbors model [14]. [PERSON] et al. suggested a nonlinear observer-based technique called Advanced Fuzzy Sliding Mode Observer (AFSMO) to optimize the average fault detection performance using a decision tree model [15]. In the field of deep learning, [PERSON] et al. used the second-generation wavelet transform to strengthen the accuracy and robustness of fault diagnosis using deep learning techniques [16]. [PERSON] et al. conducted a benchmark study in the field of deep learning, in which they evaluated four models: a multilayer perceptron, an autoencoder, a convolutional neural network and a recurrent neural network [17]. [PERSON] et al. proposed to generate wavelet images of defect signatures from bearing signals, which allow the visualization of distinctive patterns associated with different types of failures, with the aim of using them to train a convolutional neural network (CNN) [18].