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Bearing fault classification based on minimum volume ellipsoid feature extraction
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
Department of Informatics and Communications Technology, Technical Educational Institute of Epirus, 47100 Artas, Kostakioi.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-0126-1897
2013 (English)In: 2013 IEEE Multiconference on Systems and Control (MSC), Hyderabad, India, August, 28-30, 2013, 2013, p. 1177-1182Conference paper, Published paper (Refereed)
Abstract [en]

This article presents a novel fault classification and diagnosis technique for bearings based on a Minimum Volume Ellipsoid (MVE) method for feature extraction. Data from two accelerometers located at two different sights of the test bed are combined to create a two dimensional representation and the feature extraction stage condenses that information using an ellipsoid description. The proposed features feed a simple non-linear classifier which separates almost perfectly between normal and faulty conditions, with also very high diagnostic accuracy between the faulty classes. The obtained results suggest that this novel representation can be used within a condition monitoring system.

Place, publisher, year, edition, pages
2013. p. 1177-1182
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-40649Scopus ID: 2-s2.0-84902248068Local ID: fd795f8a-8219-4627-b1cf-ce13c560c335OAI: oai:DiVA.org:ltu-40649DiVA, id: diva2:1014170
Conference
IEEE Multi-Conference on Systems and Control : 28/08/2013 - 30/08/2013
Projects
Fault Detection in Bearings
Note
Godkänd; 2013; 20130515 (geonik)Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2023-10-06Bibliographically approved

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Mustafa, Mohammed ObaidNikolakopoulos, George

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CiteExportLink to record
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