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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 tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.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.
Vise andre og tillknytning
2017 (engelsk)Inngår i: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 13, nr 2, s. 625-634, artikkel-id 7778161Artikkel i tidsskrift (Fagfellevurdert) 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

sted, utgiver, år, opplag, sider
IEEE, 2017. Vol. 13, nr 2, s. 625-634, artikkel-id 7778161
Emneord [en]
Information technology - Automatic control
Emneord [sv]
Informationsteknik - Reglerteknik
HSV kategori
Forskningsprogram
Reglerteknik
Identifikatorer
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
Prosjekter
Integrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIRE
Forskningsfinansiär
EU, Horizon 2020, 636834
Merknad

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

Tilgjengelig fra: 2017-05-12 Laget: 2017-05-12 Sist oppdatert: 2018-09-14bibliografisk kontrollert

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