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An automated thermographic image segmentation method for induction motor fault diagnosis
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.ORCID iD: 0000-0001-9701-4203
Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, TEI of Epirus, Artas, Kostakioi.
Larges Drives Products RandD Department, Siemens Industry Sector - Drive Technologies, Nuremberg.
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2015 (English)In: IECON 2014: 40th Annual Conference of the IEEE Industrial Electronics Society, Dallas, TX, USA , Oct. 29 2014 - Nov. 1 2014, Piscataway, NJ: IEEE Communications Society, 2015, p. 3396-3402Conference paper, Published paper (Refereed)
Abstract [en]

Eventual failures in induction machines may lead to catastrophic consequences in terms of economic costs for the companies. The development of reliable systems for fault detection that enable to diagnose a wide range of faults is a motivation of many researchers worldwide. In this context, non-invasive condition monitoring strategies have drawn special attention since they do not require interfering with the operation process of the machine. Though the analysis of the motor currents has proven to be a reliable, non-invasive methodology to detect some of the faults (especially when assessing the rotor condition), it lacks reliability for the diagnosis of other faults (e.g. bearing faults). The infrared thermography has proven to be an excellent, non-invasive tool that can complement the diagnosis reached with the motor current analysis, especially for some specific faults. However, there are still some pending issues regarding its application to induction motor faults diagnosis, such as the lack of automation or the extraction of reliable fault indicators based on the infrared data. This paper proposes a methodology that intends to provide a solution to the first issue: a method based on image segmentation is employed to detect several failures in an automated way. Four specific faults are analyzed: bearing faults, fan failures, rotor bar breakages and stator unbalance. The results show the potential of the technique to automatically identify the fault present in the machine.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015. p. 3396-3402
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-67030Scopus ID: 2-s2.0-84949927776ISBN: 978-1-4799-4033-2 (print)OAI: oai:DiVA.org:ltu-67030DiVA, id: diva2:1166592
Conference
Annual Conference of the IEEE Industrial Electronics Society : 29/10/2014 - 01/11/2014
Available from: 2017-12-15 Created: 2017-12-15 Last updated: 2017-12-15Bibliographically approved

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CiteExportLink to record
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  • apa
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