RT info:eu-repo/semantics/article
T1 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
A1 Ruiz González, Rubén
A1 Gómez Gil, Jaime
A1 Gómez Gil, Francisco Javier
A1 Martínez-Martínez, Víctor
K1 Support Vector Machine (SVM)
K1 predictive maintenance (PdM)
K1 agricultural machinery
K1 condition monitoring
K1 fault diagnosis
K1 vibration analysis
K1 feature extraction and selection
K1 pattern recognition
K1 Vehículos
K1 Vehicles
K1 Maquinaria
K1 Machinery
AB 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.
PB MDPI
SN 1424-8220
YR 2014
FD 2014-11
LK http://hdl.handle.net/10259/4270
UL http://hdl.handle.net/10259/4270
LA eng
DS Repositorio Institucional de la Universidad de Burgos
RD 20-abr-2024