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Automatic pattern identification based on the complex empirical mode decomposition of the startup current for the diagnosis of rotor asymmetries in asynchronous machines
Laboratory of Knowledge and Intelligent Computing, Department of Informatics Engineering, Technological Educational Institute of Epirus.ORCID iD: 0000-0001-9701-4203
Large Drives R and D, Siemens Industry.
Instituto Tecnologico de la Energia, Universitat Politècnica de València.
cInstituto de Ingeniería Energética, Universitat Politècnica de València.
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2014 (English)In: IEEE transactions on industrial electronics (1982. Print), ISSN 0278-0046, E-ISSN 1557-9948, Vol. 61, no 9, p. 4937-4946, article id 6616605Article in journal (Refereed) Published
Abstract [en]

This paper presents an advanced signal processing method applied to the diagnosis of rotor asymmetries in asynchronous machines. The approach is based on the application of complex empirical mode decomposition to the measured start-up current and on the subsequent extraction of a specific complex intrinsic mode function. Unlike other approaches, the method includes a pattern recognition stage that makes possible the automatic identification of the signature caused by the fault. This automatic detection is achieved by using a reliable methodology based on hidden Markov models. Both experimental data and a hybrid simulation-experimental approach demonstrate the effectiveness of the proposed methodology

Place, publisher, year, edition, pages
Institution of Electrical Engineers of Japan (IEEJ), 2014. Vol. 61, no 9, p. 4937-4946, article id 6616605
National Category
Control Engineering
Research subject
Control Engineering
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URN: urn:nbn:se:ltu:diva-68096DOI: 10.1109/TIE.2013.2284143Scopus ID: 2-s2.0-84897381468OAI: oai:DiVA.org:ltu-68096DiVA, id: diva2:1193860
Available from: 2018-03-28 Created: 2018-03-28 Last updated: 2018-04-03Bibliographically approved

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