### Literature Review
[25] presents a technique for analyzing parasitic flux in three-phase induction motor stators combined with machine learning algorithms. Decision trees, Random Forests, and k-nearest neighbors (kNN) were used on signals captured by flow sensors. The Random Forest model achieved the highest accuracy, with 99.06%, demonstrating high robustness across different load levels and fault severities, making it an effective tool for industrial applications. Likewise, in [26], a differential protection scheme for microgrids was developed using a hybrid SVM-CNN architecture. The method was tested on lumped systems with different load and generation configurations, achieving 99.4% accuracy in fault classification, with a mean response time of 25 ms. The approach demonstrated high adaptability to topological changes and transient conditions, making it especially useful for dynamic environments. Finally, the study by [27] proposes an electrical fault detection system based on a comparison of algorithms such as SVM, kNN, and artificial neural networks. Preprocessed from a simulated network, current and voltage signals enabled the SVM model to achieve 98.3% accuracy in classifying three-phase, two-phase, and single-phase faults. This system's ability to effectively differentiate between various fault types makes it a competitive solution for advanced protection systems. In [28], the Hilbert-Huang transform (HHT) is used for fault identification and classification in alternating current transmission lines. The study used simulated data and real records, applying an artificial neural network for processing. The system was able to identify faults with 98.5% accuracy in times under 0.1 s, demonstrating high sensitivity to high-impedance faults and events located near network nodes. A more advanced strategy is presented in [29], which combines variational mode decomposition (VMD), the HHT, and convolutional neural networks (CNNs) for autonomous fault diagnosis in permanent magnet synchronous motors. Using current signals, the system identified faults in the rotor and stator with high accuracy. The CNN model achieved 99.7% accuracy on the test set, demonstrating real-time detection capabilities even under noisy and variable load conditions. Meanwhile, [30] presents an approach to fault detection and classification in photovoltaic networks, combining an optimized HHT with ensemble learning algorithms such as Random Forest and Boosting. The system achieved 99.2% accuracy in detecting faults such as arcing, short circuits, and disconnections. The model was validated using MATLAB simulations and experimental data, including scenarios with varying solar irradiance, demonstrating its applicability in real-world environments. The review in [31] performs analysis using the HHT in fault diagnosis in mechatronic devices, including motors, sensors, and nonlinear actuators. It is concluded that combining HHT with machine learning techniques improves diagnostic accuracy and early fault detection. According to the collected studies, the average accuracy exceeds 97%, and its future implementation in embedded systems with autonomous diagnostic capabilities is recommended. The work by [32] proposes an early fault detection system for BLDC motor controllers using current signals processed with the HHT. The system can identify subtle variations associated with incipient faults with an accuracy of 98.9%, maintaining its performance even under variable load and dynamic speed conditions. Tests were conducted in a laboratory environment, demonstrating the approach's effectiveness for experimental applications and its potential in industrial settings. In [33], an HHT-based approach is developed to diagnose dynamic eccentricity faults in dual-rotor axial flux generators. Signals acquired under different load and speed conditions were used, and an accuracy of 98.1% was achieved using an artificial neural network. The methodology was validated by simulation and on a physical test bench, demonstrating its feasibility for real-world operating environments. Finally, ref.