Automatic detection of early-stage electrical faults in motors is challenging because physical signals, such as temperature or vibrations, are often linked to regular machine operation. Inter-turn short-circuit (ITSC) faults in the stator winding are a leading cause of irreversible damage. Given industrial demands, there is an ongoing need for innovative approaches that reduce dimensionality and expedite feature extraction from three-phase current signals. This work presents an ITSC multi-fault classification algorithm by modeling the behavior of three-phase stator currents in a complex space. To achieve this, two geometric-based and two optimization-based approaches are employed to parameterize the shape of the complex signal. Rather than directly extracting features from the raw signals, these parameters capture the similarities among incipient failures. The algorithm is evaluated using machine learning classifiers trained on a dataset generated from an experimental test bench and a publicly available dataset. The proposed method achieved an accuracy of 95.30% across 13 categories, demonstrating its robustness and reliability and positioning it as a highly competitive alternative to state-of-the-art techniques.

The lifetime of induction motors can be significantly extended by installing diagnostic systems for monitoring their operating conditions. In particular, detecting broken bar failures in motors is important for avoiding the risk of short circuits or other accidents with serious consequences. In the literature, many approaches have been proposed for motor fault detection; however, additional generalized methods based on local and statistical analysis could provide a low-complexity and feasible solution in this field of research. The proposed work presents a methodology for detecting one or two broken rotor bars using the sums and differences histograms (SDH) and machine learning classifiers in this context. From the SDH computed in one phase of the motor’s current, nine texture features are calculated for different displacements. Then, all features are used to train two classifiers and to find the best displacements for faults and health identification in the induction motors. A final experimental evaluation considering the best displacements shows an accuracy of $98.16\%$ for the homogeneity feature and a few signal samples used in a decision tree classifier. Additionally, a polynomial regression curve validates the use of 50 samples to obtain an accuracy of $88.15\%$, whereas the highest performance is achieved for 250 samples.

The continuous advances in intelligent systems and cutting-edge technology have greatly influenced the development of intelligent vehicles. Recently, integrating multiple sensors in cars has improved and spread the advanced drive-assistance systems (ADAS) solutions for achieving the goal of total autonomy. Despite current self-driving approaches and systems, autonomous driving is still an open research issue that must guarantee the safety and reliability of drivers. This work employs images from two cameras and Global Positioning System (GPS) data to propose a 3D vision-based object localization and classification method for assisting a car during driving. The experimental platform is a prototype of a two-sitter electric vehicle designed and assembled for navigating the campus under controlled mobility conditions. Simultaneously, color and depth images from the primary camera are combined to extract 2D features, which are reprojected into 3D space. Road detection and depth features isolate point clouds representing the objects to construct the occupancy map of the environment. A convolutional neural network was trained to classify typical urban objects in the color images. Experimental tests validate car and object pose in the occupancy map for different scenarios, reinforcing the car position visually estimated with GPS measurements.

This article presents a new algorithm that serves as support for people interested in learning sign language base on the Mexican Sign Language Dictionary provided by the CONAPRED (National Council to Prevent Discrimination) in the 2011 edition. The system uses the Leap Motion machine shadow interaction system to capture the position of each finger of both hands, and by geometric modeling up to 27 signs can be statically modeled with an accuracy of 99.80%.

Color image segmentation divides the image into areas that represent different objects and focus points. One of the biggest problems in color image segmentation is the lack of homogeneity in the color of real urban images, which generates areas of over-segmentation when traditional color segmentation techniques are used. This article describes an approach to detecting and classifying objects in urban environments based on a new chromatic segmentation to locate focus points. Based on components a and b on the CIELab space, we define a chromatic map on the complex space to determine the highest threshold values by comparing neighboring blocks and thus divide various areas of the image automatically. Even though thresholds can result in broad segmentation areas, they suffice to locate centroids of patches on the color image that are then classified using a convolutional neural network (CNN). Thus, this broadly segmented image helps to crop only outlying areas instead of classifying the entire image. The CNN is trained to use six classes based on the patches drawn from the database of reference images from urban environments. Experimental results show a high score for classification accuracy that confirms the contribution of this segmentation approach.

Although the Space Phasor Model (SPM) is widely used to analyze induction motors, this study presents a new framework that applies SPM with geometric parameter extraction via a metaheuristic optimization algorithm. Differential Evolution was chosen for its simplicity, reliability, and effectiveness in exploring the parameter space. The Space Vector is constructed from the three-phase current signals. The approach captures spatial trajectories: healthy operation exhibits circular patterns, while inter-turn short-circuit (ITSC) faults deform them into ellipses, revealing both the affected phase and fault severity. The ellipse parameters are normalized using Z-score and organized into a structured fault database that includes both no-load and full-load conditions, supporting load-aware classification of 25 operating states and substantially extending the 13-class scope of previous studies. Tests with multiple classifiers (including Random Forests, Extra Trees, and gradient boosting) confirm the approach’s reliability, achieving 98.7% accuracy across 25 classes, 99.5% for the 13 most representative faults, and 94.1% under full-load for seven classes. Overall, this approach offers a phase-specific, severity-oriented diagnostic tool, highlighting its potential for industrial motor fault analysis and setting a new benchmark for multi-class classification.