Publication:
An Artificial Intelligence Approach for Gears Diagnostics in AUVs

Simple item page
dc.affiliation.areaUC3M. Área de Ingeniería Mecánicaes
dc.affiliation.dptoUC3M. Departamento de Ingeniería Mecánicaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: MAQLAB: Laboratorio de Máquinases
dc.contributor.authorNicolas Marichal, Graciliano
dc.contributor.authorDel Castillo Zas, María Lourdes
dc.contributor.authorLópez López, Jesús
dc.contributor.authorPadrón, Isidro
dc.contributor.authorArtes Gomez, Mariano
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2023-11-08T12:47:42Z
dc.date.available2023-11-08T12:47:42Z
dc.date.issued2016-04-01
dc.description.abstractIn this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles), where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised.en
dc.description.sponsorshipThis work has been supported by the Spanish Government Projects DPI2015-69325-C2-1-R, DPI2015-69325-C2-2-R of Ministerio de Economía y Competitividad, Project 2015/0001018 ULL and 2016-MEC-25-ETSII-UNED.en
dc.format.extent14es
dc.identifier.bibliographicCitationMarichal, G., Del Castillo, M. L., López, J. R., Padrón, I., & Artés, M. (2016). An artificial intelligence approach for gears diagnostics in AUVs. Sensors, 16(4), 529.en
dc.identifier.doihttps://doi.org/10.3390/s16040529
dc.identifier.issn1424-3210
dc.identifier.publicationfirstpage1es
dc.identifier.publicationissue4, 529es
dc.identifier.publicationlastpage14es
dc.identifier.publicationtitleSensorsen
dc.identifier.publicationvolume16es
dc.identifier.urihttps://hdl.handle.net/10016/38795
dc.identifier.uxxiAR/0000018684
dc.language.isoenges
dc.publisherMPDIen
dc.relation.projectIDGobierno de España. DPI2015-69325-C2-1-Res
dc.relation.projectIDGobierno de España. DPI2015-69325-C2-2-Res
dc.rights© 2016 by the authors; licensee MDPI, Basel, Switzerland.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaIngeniería Mecánicaes
dc.subject.otherVibrationen
dc.subject.otherAuvsen
dc.subject.otherVibrationen
dc.subject.otherAlgorithmen
dc.subject.otherSystemen
dc.subject.otherAnfisen
dc.subject.otherCondition monitoringen
dc.subject.otherGenetic neuro-fuzzy systemsen
dc.subject.otherFuzzy logicen
dc.subject.otherFault-tolerant controlen
dc.subject.otherNeural-networksen
dc.subject.otherUnderwater vehiclesen
dc.subject.otherFuzzy inferenceen
dc.titleAn Artificial Intelligence Approach for Gears Diagnostics in AUVsen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
artificial_S_2016.pdf
Size:
1.37 MB
Format:
Adobe Portable Document Format
Download
Collections
DIM - MAQLAB - Artículos de revistas