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dc.contributor.author | López-Estrada, F. R.![]() |
es_ES |
dc.contributor.author | Méndez-López, A.![]() |
es_ES |
dc.contributor.author | Santos-Ruiz, I.![]() |
es_ES |
dc.contributor.author | Valencia-Palomo, G.![]() |
es_ES |
dc.contributor.author | Escobar-Gómez, E.![]() |
es_ES |
dc.date.accessioned | 2021-07-07T08:25:25Z | |
dc.date.available | 2021-07-07T08:25:25Z | |
dc.date.issued | 2021-07-01 | |
dc.identifier.issn | 1697-7912 | |
dc.identifier.uri | http://hdl.handle.net/10251/168908 | |
dc.description.abstract | [EN] This work proposes an actuator fault detection and isolation scheme for a quadrotor unmanned aerial vehicle (UAV) under a data-driven approach using machine learning techniques. In this approach, an implicit model of the system is built through the information provided by the onboard sensors of the UAV. First, using a tailored flying platform, vibrations corresponding to the orientation, angular position and linear acceleration were captured with the UAV flying in hover mode under nominal conditions. This data is processed by Principal Component Analysis (PCA) for feature extraction. Subsequently, faults in the actuators are induced through a cut in each of the UAV propellers which generate a reduction in the thrust of the rotors. These data are also projected into the PCA subspace and compared to the nominal data. Hotelling’s T 2 statistic is used to discern between nominal data and data when the vehicle exhibits an actuator fault. Finally, the developed algorithms were complemented with k-nearest neighbors (k-NN) and support vector machine (SVM) classification algorithms. The results show a correct classification rate of 89.6 % (k-NN) and 92.4 % (SVM) respectively for 423 validation datasets. | es_ES |
dc.description.abstract | [ES] 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. | es_ES |
dc.language | Español | es_ES |
dc.publisher | Universitat Politècnica de València | es_ES |
dc.relation.ispartof | Revista Iberoamericana de Automática e Informática industrial | es_ES |
dc.rights | Reconocimiento - No comercial - Compartir igual (by-nc-sa) | es_ES |
dc.subject | Unmanned aerial vehicle | es_ES |
dc.subject | Fault detection and isolation | es_ES |
dc.subject | Principal component analisys | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Quadrotor | es_ES |
dc.subject | Vehículo aéreo no tripulado | es_ES |
dc.subject | Detección e identificación de fallas | es_ES |
dc.subject | Análisis en componentes principales | es_ES |
dc.subject | Aprendizaje de máquina | es_ES |
dc.subject | Cuadrirrotor | es_ES |
dc.title | Detección de fallas en vehículos aéreos no tripulados mediante señales de orientación y técnicas de aprendizaje de máquina | es_ES |
dc.title.alternative | Fault detection in unmanned aerial vehicles via orientation signals and machine learning | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.4995/riai.2020.14031 | |
dc.rights.accessRights | Abierto | es_ES |
dc.description.bibliographicCitation | López-Estrada, FR.; Méndez-López, A.; Santos-Ruiz, I.; Valencia-Palomo, G.; Escobar-Gómez, E. (2021). Detección de fallas en vehículos aéreos no tripulados mediante señales de orientación y técnicas de aprendizaje de máquina. Revista Iberoamericana de Automática e Informática industrial. 18(3):254-264. https://doi.org/10.4995/riai.2020.14031 | es_ES |
dc.description.accrualMethod | OJS | es_ES |
dc.relation.publisherversion | https://doi.org/10.4995/riai.2020.14031 | es_ES |
dc.description.upvformatpinicio | 254 | es_ES |
dc.description.upvformatpfin | 264 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 18 | es_ES |
dc.description.issue | 3 | es_ES |
dc.identifier.eissn | 1697-7920 | |
dc.relation.pasarela | OJS\14031 | es_ES |
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