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Failure diagnosis of railway assets using support vector machine and ant colony optimization method
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-4107-0991
2012 (English)In: International Journal of COMADEM, ISSN 1363-7681, Vol. 15, no 2, p. 3-10Article in journal (Refereed) Published
Abstract [en]

Support Vector Machine (SVM) is an excellent technique for pattern recognition. This paper uses a multi-class SVM as a classifier to solve a multi-class classification problem for fault diagnosis. As the pre-defined parameters in the SVM influence the performance of the classification, this paper uses the heuristic Ant Colony Optimization (ACO) algorithm to find the optimal parameters. This multi-class SVM and ACO are applied to the fault diagnosis of an electric motor used in a railway system. A case study illustrates how efficient the ACO is in finding the optimal parameters. By using the optimal parameters from the ACO, the accuracy of the performed diagnosis on the electric motor is found to be highest.

Place, publisher, year, edition, pages
2012. Vol. 15, no 2, p. 3-10
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-7291Local ID: 5a2abf60-c7c9-47f1-bf9e-b2b15fb95d45OAI: oai:DiVA.org:ltu-7291DiVA, id: diva2:980180
Note
Validerad; 2012; 20120813 (ysko)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved

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Fuqing, YuanKumar, UdayGalar, Diego

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