Detección de fallas en vehículos aéreos no tripulados mediante señales de orientación y técnicas de aprendizaje de máquina

F. R. López-Estrada, A. Méndez-López, I. Santos-Ruiz, G. Valencia-Palomo, E. Escobar-Gómez

Resumen

Este trabajo propone un esquema de detección y localización de fallas en los actuadores de un vehículo aéreo no tripulado (VANT) del tipo cuadrirrotor. Para ello, se considera un enfoque basado en datos haciendo uso de técnicas de aprendizaje de máquina. En este enfoque se construye un modelo implícito del sistema a través de la información proporcionada por los sensores del VANT. Primero, a través de un plataforma de vuelo de tipo giroscópica, se captan las vibraciones correspondientes a la orientación, posición angular y aceleración lineal cuando el vehículo se encuentra en vuelo estacionario en condiciones nominales. Estos datos se procesan mediante Análisis en Componentes Principales (PCA) para la extracción de características. Posteriormente, se induce una falla a los actuadores a través de un recorte en cada una de las hélices del VANT que ocasionan una reducción del empuje generado por los rotores. Estos datos se proyectan también al subespacio de componentes principales y se comparan con los datos nominales. Para discernir entre los datos nominales y los datos cuando el vehículo presenta falla, se emplea el estadístico T2 de Hotelling. Finalmente, el desarrollo se complementa con los algoritmos de clasificación de k-vecinos más cercanos (k-NN) y de máquina de vectores de soporte (SVM). Los resultados muestran una tasa de clasificación correcta del 89.6 % (k-NN) y 92.4 %(SVM) respectivamente para 423 conjuntos de datos de validación.


Palabras clave

Vehículo aéreo no tripulado; detección e identificación de fallas; análisis en componentes principales; aprendizaje de máquina; cuadrirrotor

Clasificación por materias

Inteligencia computacional y técnicas de supervisión y detección de fallos aplicados al control automático de sistemas

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