Fundamental frequency suppression for the detection of broken bar in induction motors at low slip and frequency

Producción CientíficaBroken rotor bar (BRB) is one of the most common failures in induction motors (IMs) these days; however, its identification is complicated since the frequencies associated with the fault condition appear near the fundamental frequency component (FFC). This situation gets worse when the IM slip or the operation frequency is low. In these circumstances, the common techniques for condition monitoring may experience troubles in the identification of a faulty condition. By suppressing the FFC, the fault detection is enhanced, allowing the identification of BRB even at low slip conditions. The main contribution of this work consists of the development of a preprocessing technique that estimates the FFC from an optimization point of view. This way, it is possible to remove a single frequency component instead of removing a complete frequency band from the current signals of an IM. Experimentation is performed on an IM operating at two different frequencies and at three different load levels. The proposed methodology is compared with two different approaches and the results show that the use of the proposed methodology allows to enhance the performance delivered by the common methodologies for the detection of BRB in steady state.CONACyT scholarship (415315)Project FOFI-UAQ 2018 FIN201812PRODEP UAQ-PTC-385 gran

Power consumption analysis of electrical installations at healthcare facility

Producción CientíficaThis paper presents a methodology for power consumption estimation considering harmonic and interharmonic content and then it is compared to the power consumption estimation commonly done by commercial equipment based on the fundamental frequency, and how they can underestimate the power consumption considering power quality disturbances (PQD). For this purpose, data of electrical activity at the electrical distribution boards in a healthcare facility is acquired for a long time period with proprietary equipment. An analysis in the acquired current and voltage signals is done, in order to compare the power consumption centered in the fundamental frequency with the generalized definition of power consumption. The results obtained from the comparison in the power consumption estimation show differences between 4% and 10% of underestimated power consumption. Thus, it is demonstrated that the presence of harmonic and interharmonic content provokes a significant underestimation of power consumption using only the power consumption centered at the fundamental frequency.SEP-CONACYT, under grant 222453-2013FOMIX, under grant QRO-2014-C03-250269FOFIUAQ-FIN20161

Genetic algorithm methodology for the estimation of generated power and harmonic content in photovoltaic generation

Producción CientíficaRenewable generation sources like photovoltaic plants are weather dependent and it is hard to predict their behavior. This work proposes a methodology for obtaining a parameterized model that estimates the generated power in a photovoltaic generation system. The proposed methodology uses a genetic algorithm to obtain the mathematical model that best fits the behavior of the generated power through the day. Additionally, using the same methodology, a mathematical model is developed for harmonic distortion estimation that allows one to predict the produced power and its quality. Experimentation is performed using real signals from a photovoltaic system. Eight days from different seasons of the year are selected considering different irradiance conditions to assess the performance of the methodology under different environmental and electrical conditions. The proposed methodology is compared with an artificial neural network, with the results showing an improved performance when using the genetic algorithm methodology.CONACYT (scholarship 415315)FOFI –UAQ 2018 (project FIN201812)PRODEP (project UAQ-PTC-385

DETECCIÓN DE FALLA DE RODAMIENTO EN UNA CADENA CINEMÁTICA VÍA EMISIÓN ACÚSTICA

ResumenLas cadenas cinemáticas son componentes esenciales en la mayoría industrias, compuestas principalmente por motores de inducción, cajas de engranes, etc.., las fallas de estás provocan grandes pérdidas monetarias. Para evitarlos se utilizan sistemas automatizados de monitorización. Existen diferentes técnicas de monitoreo con diferentes metodologías, la emisión acústica (EA) es uno de los métodos de monitoreo no invasivo para la detección de fallas en estos sistemas. En este trabajo se presenta el desarrollo de un sistema de adquisición de señales de EA y una metodología basada en el análisis de estas señales para la detección de falla de rodamiento en un banco de pruebas de una cadena cinemática, la identificación de los componentes relacionados con la falla para el análisis es respaldado por su modelo teórico. Los resultados obtenidos muestran la detección de falla en rodamiento en altas frecuencias y la metodología para el análisis de la EA.Palabras Claves: Detección de fallas, emisión acústica, FFT, rodamientos. DETECTION OF BEARING FAILURE IN A CINEMATIC CHAIN VIA ACOUSTIC EMISSIONAbstractKinematics Chains are essential components in most industries, composed mainly of induction motors, gearboxes, etc.., failures within them cause great monetary losses. To avoid this, automated monitoring systems are used. There are different monitoring techniques with different methodologies, the acoustic emission (AE) is one of the methods of noninvasive monitoring for the detection of failures in these systems. This work presents the development of an AE signal acquisition system and a methodology based on the analysis of these signals for the detection of bearing failure in a test bench of a kinematic chain. The identification of the components related to the fault for the analysis is supported by its theoretical model. The obtained results show the detection of failure in rolling in high frequencies and the methodology for the analysis of the AE. Keywords: Acoustic emission, bearings, faults detection, FFT

Spectral kurtosis based methodology for the identification of stationary load signatures in electrical signals from a sustainable building

Producción CientíficaThe increasing use of nonlinear loads in the power grid introduces some unwanted effects, such as harmonic and interharmonic contamination. Since the existence of spectral contamination causes waveform distortion that may be harmful to the loads that are connected to the grid, it is important to identify the frequency components that are related to specific loads in order to determine how relevant their contribution is to the waveform distortion levels. Due to the diversity of frequency components that are merged in an electrical signal, it is a challenging task to discriminate the relevant frequencies from those that are not. Therefore, it is necessary to develop techniques that allow performing this selection in an efficient way. This paper proposes the use of spectral kurtosis for the identification of stationary frequency components in electrical signals along the day in a sustainable building. Then, the behavior of the identified frequencies is analyzed to determine which of the loads connected to the grid are introducing them. Experimentation is performed in a sustainable building where, besides the loads associated with the normal operation of the building, there are several power electronics equipment that is used for the electric generation process from renewable sources. Results prove that using the proposed methodology it is possible to detect the behavior of specific loads, such as office equipment and air conditioning.Universidad de Valladolid y Consejo Mexicano de Ciencia y Tecnología (CONACYT) - (grant 743842)Universidad Autónoma de Querétaro, Fondo para el Desarrollo del Conocimiento (FONDEC-UAQ 2020) - (project FIN202011

A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging

Through the increasing use of unmanned aerial vehicles as remote sensing tools, shadows become evident in aerial imaging; this fact, alongside the higher spatial resolution obtained by high-resolution mounted cameras, presents a challenging issue when performing different image processing tasks related to urban areas monitoring. Accordingly, the state-of-the-art reported works can correct the shadow regions, but the heterogeneity between the corrected shadow and non-shadow areas is still evident and especially noticeable in concrete and asphalt regions. The present work introduces a local color transfer methodology to shadow removal which is based on the CIE L*a*b (Lightness, a and b) color space that considers chromatic differences in urban regions, and it is followed by a color tuning using the HSV color space. The quantitative comparison was executed by using the shadow standard deviation index (SSDI), where the proposed work provided low values that improve up to 19 units regarding other tested methods. The qualitative comparison was visually realized and proved that the proposed method enhances the color correspondence without losing texture information. Quantitative and qualitative results validate the results of color correction and texture preservation accuracy of the proposed method against other published methodologies