Constructing feature-based ensemble classifiers for real-world machines fault diagnosis

de Oliveira, Marcelo V. and Estefhan, D. Wandekokem and Mendel, Eduardo and Fabris, Fabio and Flavio, M. Varejao and Thomas, W. Rauber and Rodrigo, J. Batista (2010) Constructing feature-based ensemble classifiers for real-world machines fault diagnosis. In: IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society. IEEE pp. 1099-1104. ISBN 978-1-4244-5225-5. E-ISBN 978-1-4244-5226-2. (doi:https://doi.org/10.1109/IECON.2010.5675522) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
http://dx.doi.org/10.1109/IECON.2010.5675522

Abstract

This paper presents the results achieved by fault classifier ensembles based on supervised learning for diagnosing faults on oil rigs motor pumps. The main goal is to apply two feature-based ensemble construction methods to a real-world problem. Recent studies have shown that the use of ensembles of classifiers that are accurate and at the same time have diversifying results can improve the final classification accuracy, compared to a single accurate classifier. The diversification performed by the methods presented in this work is obtained by varying the feature set each classifier uses. We show results obtained with the established genetic algorithm GEFS and a recently developed approach called BSFS, which has lower computational cost. We rely on a database of real data, with 2000 acquisitions of vibration signals extracted from operational motor pumps. Our results show that the ensemble methods had a higher classification accuracy solving a real-world fault diagnosis problem than single classifiers. Also, we present promising results in our experiments with both algorithms, that successfully solves the problem.

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76 Computer software
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: F. Fabris
Date Deposited: 08 Dec 2013 14:43 UTC
Last Modified: 14 Sep 2018 14:52 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/37379 (The current URI for this page, for reference purposes)
Fabris, Fabio: https://orcid.org/0000-0001-7159-4668
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