Aplicación de una red neuronal feed-forward backpropagation para el diagnóstico de fallas mecánicas en motores de encendido provocado

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Wilmer Rafael Contreras Urgiles http://orcid.org/0000-0003-2300-9457
José Maldonado Ortega http://orcid.org/0000-0002-3846-2599
Rogelio León Japa http://orcid.org/0000-0003-2142-3769

Keywords

diagnóstico, fallos mecánicos, red feed-forward backpropagation, ANOVA, matriz de correlación, Random Forest

Resumen

En la presente investigación se explica la metodología para la creación de un sistema de diagnóstico aplicado a la detección de fallas mecánicas en vehículos con motores a gasolina mediante redes neuronales artificiales, el sistema se basa en el estudio de la fase de admisión del ciclo Otto, el cual es registrado a través de la implementación física de un sensor MAP (Manifold Absolute Pressure). Se emplea un estricto protocolo de muestreo y su correspondiente análisis estadístico. Los valores estadísticos de la señal del sensor MAP: área, energía, entropía, máximo, media, mínimo, potencia y RMS se seleccionaron en función al mayor aporte de información y diferencia significativa. Los datos se obtuvieron con la aplicación de 3 métodos estadísticos (ANOVA, matriz de correlación y Random Forest) para tener una base de datos que permita el entrenamiento de una red neuronal feed-forward backpropagation, con la cual se obtiene un error de clasificación de 1.89e-11. La validación del sistema de diagnóstico se llevó a cabo mediante la provocación de fallas supervisadas en diferentes motores de encendido provocado.
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