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Sensors 2014, 14(11), 20713-20735; doi:10.3390/s141120713

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

1
Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, Valladolid 47011, Spain
2
Department of Electromechanical Engineering, University of Burgos, Burgos 09006, Spain
*
Author to whom correspondence should be addressed.
Received: 9 September 2014 / Revised: 20 October 2014 / Accepted: 23 October 2014 / Published: 3 November 2014
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
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Abstract

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. View Full-Text
Keywords: Support Vector Machine (SVM); predictive maintenance (PdM); agricultural machinery; condition monitoring; fault diagnosis; vibration analysis; feature extraction and selection; pattern recognition Support Vector Machine (SVM); predictive maintenance (PdM); agricultural machinery; condition monitoring; fault diagnosis; vibration analysis; feature extraction and selection; pattern recognition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ruiz-Gonzalez, R.; Gomez-Gil, J.; Gomez-Gil, F.J.; Martínez-Martínez, V. 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. Sensors 2014, 14, 20713-20735.

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