### Literature Review
Its applicability to the diagnosis of other rotating machines is also discussed. In the study by [20], a high-impedance fault detection method is developed using a combination of the Hilbert and Fourier transforms. These types of faults, difficult to detect using conventional techniques due to their low fault currents, were addressed through time-frequency domain characteristic analysis. The proposed model achieved detection rates above 90% in simulations of typical distribution networks. Its high sensitivity to minor signal variations makes it a viable option for real-time applications, especially in urban environments. Furthermore, the study suggests its integration with intelligent sensors to improve electrical safety. Finally, the work of [21] presents a fault diagnosis system in electric drives based on a Deep Echo State Network (DESN)-type neural network with scaled activation functions. The approach was validated with a database that includes multiple types of faults, such as insulation faults, phase imbalance, and rotor bar breakage. The model achieved an accuracy of 98.5% with an inference time of less than 15 ms, standing out for its speed and efficiency compared to traditional models such as RNN and LSTM. Furthermore, it demonstrated robustness against noise and uncertainty and is considered highly efficient for implementation in embedded monitoring and protection systems. An innovative approach to fault detection in transmission lines is presented in [22], which uses 2D convolutional neural networks (2D-CNNs) applied to scalogram imagesgenerated using the continuous wavelet transform. The study considered five types of faults, including LG, LL, and LLG, and generated 3600 images for training and testing. The four-layer CNN model achieved a classification accuracy of 99.46%, identifying the location and type of fault in less than 0.1 s. The system demonstrated viability for real-time applications integrated with SCADA platforms. It maintained its performance under conditions with a signal-to-noise ratio of 20 dB, demonstrating robustness in noisy environments. In [23], the detection of intermittent ground faults in compensated and isolated medium-voltage networks is addressed using data-driven models. Unlike conventional methods that rely on zero-sequence current, this approach combines voltage and current signals with machine learning algorithms, specifically Random Forest and XGBoost. Tests conducted with real event records yielded detection rates above 96% for faults lasting less than 200 ms. The model's adaptability to smart grids positions it as a promising alternative for improving operational reliability. An extensive review of fault diagnosis techniques in distribution networks is presented in [24], which analyzes more than 110 recent publications on artificial intelligence and signal processing. The study ranges from classical methods such as SVM, ANN, and CNN to advanced transforms such as Hilbert-Huang and wavelet. Average accuracy of 97% in controlled environments and the effective use of differential current signals and spectral analysis are highlighted. In addition, hybrid approaches are recommended to improve multiple fault detection, and significant gaps in the availability of labeled data are identified, especially for non-conventional faults. In [11], an adaptive protection technique is proposed for active distribution networks with microgrids integrating multiple distributed generation (DER) technologies. Using PSCAD simulations, the system's performance was evaluated against internal and external faults. The results reveal significant improvements in selectivity and detection speed, reducing the response time to less than 20 ms compared to the 60 ms observed in conventional schemes. The methodology is based on adaptive logic for microgrid status, complemented by continuous current and voltage monitoring, making it especially suitable for urban hybrid networks. Regarding fault diagnosis in electrical machines, ref.