Influencia del mantenimiento predictivo en los indicadores de mantenimiento de motores de Retroexcavadora Caterpillar 420e
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Date
2025-04
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Universidad Nacional de Trujillo
Abstract
La presente investigación tuvo como objetivo evaluar la influencia del mantenimiento
predictivo en los indicadores de mantenimiento de los motores de las retroexcavadoras
Caterpillar 420E. Se realizó un análisis inicial de los indicadores clave, como el Tiempo Medio
Entre Fallas (MTBF), el Tiempo Medio de Reparación (MTTR) y la Disponibilidad, obteniendo
valores promedio de 91.57 horas, 13.50 horas, y un rango de disponibilidad del 79.88% al
92.75% antes de la implementación del plan. Posteriormente, se diseñó e implementó un plan
de mantenimiento predictivo basado en el monitoreo de parámetros críticos del sistema de
lubricación, como presión, temperatura, viscosidad y contaminación por partículas metálicas,
junto con inspecciones visuales periódicas y análisis de aceite.
La metodología empleada incluyó el análisis de criticidad mediante diagramas de Pareto y
matrices de evaluación, identificando al sistema de lubricación como el subsistema más crítico.
El plan fue ejecutado durante seis meses, permitiendo monitorear en tiempo real los cambios
en los parámetros operativos y evaluar su impacto en el desempeño de las unidades. Los
resultados demostraron una mejora significativa en los indicadores, alcanzando un incremento
promedio de 6% en disponibilidad, un aumento en el MTBF a 101.82 horas y una reducción
en el MTTR a 10.75 horas.
Desde el punto de vista financiero, el análisis económico mostró que la implementación del
plan generó un ahorro total proyectado de S/. 75,000, derivado de la reducción de fallas y el
aumento en la productividad. Esto validó la viabilidad económica del mantenimiento predictivo
como estrategia para optimizar la eficiencia operativa.
Se concluye que el mantenimiento predictivo aplicado a los motores de las retroexcavadoras
Caterpillar 420E no solo mejora los indicadores operativos, sino que también contribuye a
reducir los costos de mantenimiento. Se recomienda continuar con el monitoreo periódico de
los parámetros del sistema de lubricación, ampliar la aplicación del modelo predictivo a otros
subsistemas críticos e implementar tecnologías avanzadas para fortalecer la estrategia de
mantenimiento basada en la condición.
The objective of this research was to evaluate the influence of predictive maintenance on the maintenance indicators of Caterpillar 420E backhoe loader engines. An initial analysis of key indicators, such as Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR), and Availability, revealed average values of 91.57 hours, 13.50 hours, and an availability range between 79.88% and 92.75% before implementing the plan. Subsequently, a predictive maintenance plan was designed and implemented, focusing on monitoring critical parameters of the lubrication system, such as pressure, temperature, viscosity, and metallic particle contamination, along with periodic visual inspections and oil analysis. The methodology included a criticality analysis using Pareto charts and evaluation matrices, identifying the lubrication system as the most critical subsystem. The plan was executed over six months, enabling real-time monitoring of operational parameters and assessing their impact on unit performance. The results showed significant improvements in indicators, achieving an average 6% increase in availability, an increase in MTBF to 101.82 hours, and a reduction in MTTR to 10.75 hours. From a financial perspective, the economic analysis demonstrated that the implementation of the plan resulted in total projected savings of S/. 75,000, derived from reduced failures and increased productivity. This validated the economic feasibility of predictive maintenance as a strategy to optimize operational efficiency. It is concluded that predictive maintenance applied to the engines of Caterpillar 420E backhoe loaders not only improves operational indicators but also reduces maintenance costs. It is recommended to continue the periodic monitoring of lubrication system parameters, expand the application of the predictive model to other critical subsystems, and implement advanced technologies to enhance the condition-based maintenance strategy.
The objective of this research was to evaluate the influence of predictive maintenance on the maintenance indicators of Caterpillar 420E backhoe loader engines. An initial analysis of key indicators, such as Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR), and Availability, revealed average values of 91.57 hours, 13.50 hours, and an availability range between 79.88% and 92.75% before implementing the plan. Subsequently, a predictive maintenance plan was designed and implemented, focusing on monitoring critical parameters of the lubrication system, such as pressure, temperature, viscosity, and metallic particle contamination, along with periodic visual inspections and oil analysis. The methodology included a criticality analysis using Pareto charts and evaluation matrices, identifying the lubrication system as the most critical subsystem. The plan was executed over six months, enabling real-time monitoring of operational parameters and assessing their impact on unit performance. The results showed significant improvements in indicators, achieving an average 6% increase in availability, an increase in MTBF to 101.82 hours, and a reduction in MTTR to 10.75 hours. From a financial perspective, the economic analysis demonstrated that the implementation of the plan resulted in total projected savings of S/. 75,000, derived from reduced failures and increased productivity. This validated the economic feasibility of predictive maintenance as a strategy to optimize operational efficiency. It is concluded that predictive maintenance applied to the engines of Caterpillar 420E backhoe loaders not only improves operational indicators but also reduces maintenance costs. It is recommended to continue the periodic monitoring of lubrication system parameters, expand the application of the predictive model to other critical subsystems, and implement advanced technologies to enhance the condition-based maintenance strategy.
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TECHNOLOGY::Engineering mechanics