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The use of a multilabel classification framework for the detection of broken bars and mixed eccentricity faults based on the start-up transient
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-9701-4203
Department of Electrical Engineering and Automation, Aalto University.
Instituto Tecnologico de la Energia, Universitat Politècnica de València.
ABB Corporate Research, Baden-Dättwil.
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2017 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 13, no 2, p. 625-634, article id 7778161Article in journal (Refereed) Published
Abstract [en]

In this paper, a data-driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multilabel classification problem, with each label corresponding to one specific fault. The faulty conditions examined include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity, while three 'problem transformation' methods are tested and compared. For the feature extraction stage, the start-up current is exploited using two well-known time-frequency (scale) transformations. This is the first time that a multilabel framework is used for the diagnosis of co-occurring fault conditions using information coming from the start-up current of induction motors. The efficiency of the proposed approach is validated using simulation data with promising results irrespective of the selected time-frequency transformation

Place, publisher, year, edition, pages
IEEE, 2017. Vol. 13, no 2, p. 625-634, article id 7778161
Keywords [en]
Information technology - Automatic control
Keywords [sv]
Informationsteknik - Reglerteknik
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-63330DOI: 10.1109/TII.2016.2637169ISI: 000399961500021Scopus ID: 2-s2.0-85018158059OAI: oai:DiVA.org:ltu-63330DiVA, id: diva2:1095105
Projects
Integrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIRE
Funder
EU, Horizon 2020, 636834
Note

Validerad; 2017; Nivå 2; 2017-05-12 (andbra)

Available from: 2017-05-12 Created: 2017-05-12 Last updated: 2018-09-14Bibliographically approved

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Georgoulas, GeorgiosNikolakopoulos, George

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