Online monitoring of induction motors has increased significantly in recent years because these devices are essential components of any industrial process. Incipient fault detection in induction motors avoids interruptions in manufacturing processes and facilitates maintenance tasks to reduce induction motor timeout. Therefore, the proposal of novel approaches to assist in the detection and classification of induction motor faults is in order. In this work, a reliable and noninvasive novel technique that does not require computational demanding operations, since it just performs arithmetic calculations, is introduced for detecting and locating short-circuit faults in the stator windings of an induction motor. This method relies on phasor analysis and the RMS values of line currents, followed by a small set of simple if-then rules to perform the diagnosis and identification of stator winding faults. Obtained results from different experimental tests on a rewound induction motor stator to induce short-circuit faults demonstrate that the proposed approach is capable of identifying and locating incipient and advanced deficiencies in the windings’ insulation with high effectiveness.

Induction motors (IM) play essential tasks in distinct production sectors because of their low cost and robustness. Considering that most of the energy demand in industry is allocated for powering up IM, recent research has focused on detecting and predicting faults to avoid severe disturbances. Broken rotor bars (BRB) in IM cause a significant deficit of energy, above all in those applications where constant changes in speed are required, increasing the probability of a catastrophic failure. Variable speed drives (VSD) introduce harmonic components to the power supply current controlling the IM rotating speed, which make it difficult to identify BRB. Therefore, in this work, an innovative methodology is proposed for detecting BRB in VSD-fed IM with a wide rotating-speed bandwidth during their start-up transient. The introduced procedure performs a statistical analysis for computing the mean, median, mode, variance, skewness, and kurtosis, to identify slight changes on the acquired current signal. These values are fed into an artificial neural network (ANN), which carries out the IM operational condition classification as healthy (HLT) or with BRB. Experimentally obtained results corroborate the effectiveness of the proposed approach to detecting BRB even for dynamically varying rotating speed, reaching a high accuracy of 99%, similar to recently reported techniques.

El impacto de la tecnología digital en las empresas y la dinámica del mercado ha sido fundamental en la transformación de las organizaciones. Esta tecnología ha impulsado la forma en que las empresas se organizan y compiten, ofreciendo ventajas competitivas a través de la reestructuración de la industria, la creación de nuevos modelos de negocios y la formulación de estrategias para permanecer en el mercado. Las tecnologías digitales han revolucionado la eficiencia operativa al reducir los costos, han permitido el análisis de grandes volúmenes de datos para tomar decisiones más informadas y han transformado la comercialización de productos y la cadena de suministro, evolucionando de la economía de la información a la economía del conocimiento. Este libro reflexiona sobre la digitalización empresarial, el papel de la tecnología, la economía social y el espíritu emprendedor como factores esenciales para el desarrollo de las organizaciones. En particular, en una sociedad altamente interconectada que demanda nuevas formas de producción y de consumo, la tecnología ofrece la capacidad de adaptarse al entorno actual. Las empresas se ven obligadas a transformar sus procesos y productos, y a adoptar nuevas estrategias de comercialización, enfoques de gestión de recursos humanos y relaciones con los clientes, transformando así la cultura organizacional.

Multiphoton (MP) microscopy is a well-established method for the non-invasive imaging of biological tissues. However, its optical sectioning capabilities are reduced due to specimen-induced aberrations. Both the manipulation of spherical aberration (SA) and the use of axicons have been reported to be useful techniques to bypass this limitation. We propose the combination of SA patterns and variable axicons to further improve the quality of MP microscopy images. This approach provides enhanced images at different depth locations whose quality is better than those corresponding to the use of SA or axicons separately. Thus, the procedure proposed herein facilitates the visualization of details and increases the depth observable at high resolution.

Research has shown that longitudinal chromatic aberration (LCA) of the human eye is generated across all of the eye’s optical surfaces. However, it may not be necessary to measure the LCA from the first surface of the cornea to the retina, as it is known that most of the changes that can modify the path of light occur from the first surface of the cornea to the last surface of the crystalline lens. This investigation presents the study of an objective technique that allows the measurement of longitudinal chromatic aberration (LCA) on the last crystalline lens surface by developing a pulse width wavefront system using a Hartmann test, Purkinje image, and Zernike polynomial. A blue pulse (440–480 nm) and a red pulse (580–640 nm) were used to generate a pattern of spots in the human eye. This pattern generated on the posterior surface of the crystalline lens of the human eye allows the reconstruction of the wavefront via a modal method with Zernike polynomials. Once the wavefront is reconstructed, Zernike coefficients can be used to quantify the LCA. The methodology and objective measurements of the magnitude of the longitudinal chromatic aberration of five test subjects are explained in this article.

Identification is considered a very important procedure, within the control area, to estimate the best-possible approximate model among different designs. Its significance comes from the fact that more than 75% of the cost associated with an advanced control project is aimed at obtaining a precise mathematical modeling. Therefore, in this work, an exhaustive analysis was carried out to determine the appropriate input stimulus for an unknown real system that must be controlled, with the aim of accurately estimating its transfer function (TF) using the empirical identification method (gray-box). The analysis was performed quantitatively by means of three tests: (i) the PID controller step response was evaluated theoretically; (ii) the controller performance was assessed in a Cartesian robot by tracking a trajectory defined through a Gaussian acceleration profile; (iii) the efficiency of the determined input stimulus with the best performance on inferring the TF for the system to be controlled was verified by assessing its operation in a real system, through repeatability tests, utilizing the integral errors.