Visión inteligente para el control de calidad in situ de lubricantes en vehículos militares desplegados a zona cero de operaciones
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2026
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06/03/2025
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Universidad Complutense de Madrid
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Este estudio explora la aplicación de técnicas avanzadas de inteligencia artificial en el análisis de aceites lubricantes usados para el mantenimiento predictivo de motores y cajas de cambios en vehículos militares. Dado que los aceites lubricantes son fundamentales para el funcionamiento y la durabilidad de estos sistemas, la investigación se enfoca en los desafíos asociados con su degradación y contaminación, ya que dichos factores pueden evidenciar el estado de los componentes críticos y ayudar a prevenir fallos operativos. Además, reducir los cambios innecesarios de aceite minimiza el impacto ambiental al optimizar la gestión de los aceites usados. Para el análisis, se utilizaron dos tipos de aceites lubricantes procedentes de vehículos militares: un aceite mineral multigrado 15W40 para motores (denominado O-1236) y un aceite sintético multigrado 5W30 para cajas de cambios (denominado O-1178). Se realizaron ensayos fisicoquímicos estandarizados para evaluar propiedades como la viscosidad, el índice de acidez, el contenido de agua y la presencia de metales desgastados. Estos ensayos permitieron clasificar los aceites en las categorías de "Conformes" y "No conformes", de acuerdo con su capacidad para operar de manera segura en los sistemas mecánicos analizados...
This study explores the application of advanced artificial intelligence (AI) techniques in the analysis of used lubricating oils for predictive maintenance of engines and gearboxes in military vehicles. Since lubricating oils are fundamental to the operation and durability of these systems, the research focuses on the challenges associated with their degradation and contamination. These factors can reveal the condition of critical components and help prevent operational failures. Additionally, reducing unnecessary oil changes minimizes environmental impact by optimizing the management of used oils.For the analysis, two types of lubricating oils from military vehicles were used: a multigrade mineral oil 15W40 for engines (designated as O-1236) and a synthetic multigrade oil 5W30 for gearboxes (designated as O-1178). Standardized physicochemical tests were conducted to evaluate properties such as viscosity, acidity index, water content, and the presence of wear metals. These tests allowed the classification of oils into “Conforming” and “Non-conforming” categories based on their ability to operate safely within the analyzed mechanical systems...
This study explores the application of advanced artificial intelligence (AI) techniques in the analysis of used lubricating oils for predictive maintenance of engines and gearboxes in military vehicles. Since lubricating oils are fundamental to the operation and durability of these systems, the research focuses on the challenges associated with their degradation and contamination. These factors can reveal the condition of critical components and help prevent operational failures. Additionally, reducing unnecessary oil changes minimizes environmental impact by optimizing the management of used oils.For the analysis, two types of lubricating oils from military vehicles were used: a multigrade mineral oil 15W40 for engines (designated as O-1236) and a synthetic multigrade oil 5W30 for gearboxes (designated as O-1178). Standardized physicochemical tests were conducted to evaluate properties such as viscosity, acidity index, water content, and the presence of wear metals. These tests allowed the classification of oils into “Conforming” and “Non-conforming” categories based on their ability to operate safely within the analyzed mechanical systems...
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Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Químicas, leída el 06-03-2025












