Aplicabilidad de la criticidad en el mantenimiento de equipos

 This work presents an information system of preventive, predictive and corrective maintenance related to the data obtained from Criticality Matrix whose parameters were based on the history of maintenance interventions and the visual and auditory observation of equipment such as Mechanical scales, Winches, Hoppers and Sterilizers. Criticality analysis determined trends of preventive and predictive maintenance for the pairs “Hoppers, Sterilizers” and “Mechanical scales, Winches”, respectively, establishing in this way a preventive maintenance planning system for Hoppers and Sterilizers through the design, elaboration and data feed to the information system that anticipates the planned control over the actions of the maintenance and production department. These actions generate benefits such as the availability and safety of the equipment plant, improvement in the quality of the products, a better register with the capacity of first hand information on the conditions of the machinery, a good capacity in quantity and quality of maintenance activities, optimization in the handling of repair parts, improvements in the design of equipment which leads to reduction of costs for maintenance.Este trabajo presenta un sistema de información de mantenimiento preventivo, predictivo y correctivo relacionado con los datos obtenidos a partir de una Matriz de Criticidad cuyos parámetros fueron basados en el historial de intervenciones de mantenimiento y la observación visual y auditiva de los equipos tales como: Básculas mecánicas, Malacates, Tolvas y Esterilizadores. El análisis de Criticidad determinó tendencias de mantenimiento preventivo y predictivo para los pares “Básculas mecánicas, Malacates” y “Tolvas, Esterilizadores”, respectivamente, estableciéndose de esta manera un sistema de planeación de mantenimiento preventivo para Tolvas y Esterilizadores mediante el diseño, elaboración y alimentación de datos al sistema de información que anticipa el control previsivo sobre las acciones del departamento de mantenimiento y producción. Estas acciones generan beneficios tales como la disponibilidad y seguridad de la planta de equipos, mejora en la calidad de los productos, un mejor registro con capacidad de información de primera mano sobre las condiciones de la maquinaria, una buena capacidad en cantidad y calidad de actividades de mantenimiento, optimización en el manejo de partes de reparación, mejoras del diseño de equipos, lo cual conduce a reducción de costos por mantenimiento

An effective method for lung cancer diagnosis from CT scan using deep learning-based support vector network

Producción CientíficaThe diagnosis of early-stage lung cancer is challenging due to its asymptomatic nature, especially given the repeated radiation exposure and high cost of computed tomography(CT). Examining the lung CT images to detect pulmonary nodules, especially the cell lung cancer lesions, is also tedious and prone to errors even by a specialist. This study proposes a cancer diagnostic model based on a deep learning-enabled support vector machine (SVM). The proposed computer-aided design (CAD) model identifies the physiological and pathological changes in the soft tissues of the cross-section in lung cancer lesions. The model is first trained to recognize lung cancer by measuring and comparing the selected profile values in CT images obtained from patients and control patients at their diagnosis. Then, the model is tested and validated using the CT scans of both patients and control patients that are not shown in the training phase. The study investigates 888 annotated CT scans from the publicly available LIDC/IDRI database. The proposed deep learning-assisted SVM-based model yields 94% accuracy for pulmonary nodule detection representing early-stage lung cancer. It is found superior to other existing methods including complex deep learning, simple machine learning, and the hybrid techniques used on lung CT images for nodule detection. Experimental results demonstrate that the proposed approach can greatly assist radiologists in detecting early lung cancer and facilitating the timely management of patients

Application of the Gaussian Model for Monitoring Scenarios and Estimation of SO₂ Atmospheric Emissions in the Salamanca Area, Bajío, Mexico

Population and industrial growth in Mexico’s Bajío region demand greater electricity consumption. The production of electricity from fuel oil has severe implications on climate change and people’s health due to SO2 emissions. This study describes the simulation of eight different scenarios for SO2 pollutant dispersion. It takes into account distance, geoenvironmental parameters, wind, terrain roughness, and Pasquill–Gifford–Turner atmospheric stability and categories of dispersion based on technical information about SO2 concentration from stacks and from one of the atmospheric monitoring stations in Salamanca city. Its transverse character, its usefulness for modeling, and epidemiological, meteorological, and fluid dynamics studies, as suggested by the models approved by the Environmental Protection Agency (EPA), show a maximum average concentration of 399 µg/m3, at an average distance of 1800 m. The best result comparison in the scenarios was scenery 8. Maximum nocturnal dispersion was shown at a wind speed of 8.4 m/s, and an SO2 concentration of 280 µg/m3 for stack 4, an atypical situation due to the geography of the city. From the validation process, a relative error of 14.7 % was obtained, which indicates the reliability of the applied Gaussian model. Regarding the mathematical solution of the model, this represents a reliable and low-cost tool that can help improve air quality management, the location or relocation of atmospheric monitoring stations, and migration from the use of fossil fuels to environmentally friendly fuels