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Título : An SVM-Based classifier for estimating the state of various rotating components in agro-industrial machinery with a vibration signal acquired from a single point on the machine chassis
Autor : Ruiz González, Rubén
Gómez Gil, Jaime
Gómez Gil, Francisco Javier
Martínez Martínez, Víctor
Publicado en: Sensors. 2014, V. 14, n. 1, p. 20713-20735
Editorial : MDPI
Fecha de publicación : nov-2014
ISSN : 1424-8220
DOI: 10.3390/s141120713
Resumen : The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i) accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii) the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii) when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels.
Palabras clave: Support Vector Machine (SVM)
predictive maintenance (PdM)
agricultural machinery
condition monitoring
fault diagnosis
vibration analysis
feature extraction and selection
pattern recognition
Materia: Vehículos
Vehicles
Maquinaria
Machinery
Licencia: http://creativecommons.org/licenses/by/4.0/
URI : http://hdl.handle.net/10259/4270
Versión del editor: http://dx.doi.org/10.3390/s141120713
Aparece en las colecciones: Artículos iAM

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