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A Symbolic Representation Approach for the Diagnosis of Broken Rotor Bars in Induction Motors
Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus.
Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus, Artas.ORCID iD: 0000-0001-9701-4203
ABB Corporate Research, Baden-Dättwil.
Instituto de Ingeniería Energética, Universitat Politècnica de València.
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2015 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 11, no 5, p. 1028-1037, article id 7175053Article in journal (Refereed) Published
Abstract [en]

One of the most common deficiencies of currently existing induction motor fault diagnosis techniques is their lack of automatization. Many of them rely on the qualitative interpretation of the results, a fact that requires significant user expertise, and that makes their implementation in portable condition monitoring devices difficult. In this paper, we present an automated method for the detection of the number of broken bars of an induction motor. The method is based on the transient analysis of the start-up current using wavelet approximation signal that isolates a characteristic component that emerges once a rotor bar is broken. After the isolation of this component, a number of stages are applied that transform the continuous-valued signal into a discrete one. Subsequently, an intelligent icon-like approach is applied for condensing the relative information into a representation that can be easily manipulated by a nearest neighbor classifier. The approach is tested using simulation as well as experimental data, achieving high-classification accuracy.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015. Vol. 11, no 5, p. 1028-1037, article id 7175053
National Category
Control Engineering
Research subject
Control Engineering
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URN: urn:nbn:se:ltu:diva-66081DOI: 10.1109/TII.2015.2463680Scopus ID: 2-s2.0-84943796589OAI: oai:DiVA.org:ltu-66081DiVA, id: diva2:1148673
Available from: 2017-10-12 Created: 2017-10-12 Last updated: 2017-11-24Bibliographically approved

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Georgoulas, Georgios

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