2024-03-29T07:59:31Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/42702022-09-05T08:17:29Zcom_10259_4268com_10259_5086com_10259_2604col_10259_4269
00925njm 22002777a 4500
dc
Ruiz González, Rubén
author
Gómez Gil, Jaime
author
Gómez Gil, Francisco Javier
author
Martínez-Martínez, Víctor
author
2014-11
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.
1424-8220
http://hdl.handle.net/10259/4270
10.3390/s141120713
Support Vector Machine (SVM)
predictive maintenance (PdM)
agricultural machinery
condition monitoring
fault diagnosis
vibration analysis
feature extraction and selection
pattern recognition
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