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Comparison of techniques based on current signature analysis to fault detection and diagnosis in induction electrical motors

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dc.contributor.author Fontes, AS
dc.contributor.author Cardoso, CAV
dc.contributor.author Oliveira, LPB
dc.contributor.editor Rajapakse, A
dc.contributor.editor Prasad, WD
dc.date.accessioned 2022-04-01T09:53:21Z
dc.date.available 2022-04-01T09:53:21Z
dc.date.issued 2016-12
dc.identifier.citation Fontes, A.S., Cardoso, C.A.V., & Oliveira, L.P.B. (2016). Comparison of techniques based on current signature analysis to fault detection and diagnosis in induction electrical motors. In W.D. Prasad & A. Rajapakse (Eds.), Proceedings of 1st International Conference on Electrical Engineering 2016 (pp. 74-79). Institute of Electrical and Electronics Engineers, Inc. https://ieeexplore.ieee.org/xpl/conhome/7818135/proceeding en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/17554
dc.description.abstract The wide use of electrical induction motors in industries throughout the world requires, increasingly, more precision in fault diagnosis. Techniques of predictive maintenance such as Motor Current Signature Analysis (MCSA) and Motor Current Square Signature Analysis (MSCSA) are used to detect and diagnose faults patterns, characterized by the stator current spectrum, in induction motors. In this article, these techniques are applied and compared for different faults in real motors, such as inter-turn short circuit in the stator winding and eccentricity in the air gap. To assist in the comparison of these patterns of the stator current spectrum with and without faults, a theoretical model of a healthy electrical induction motor was used, with the same values of the real supply voltages, which generated the frequency spectrum patterns. The results presented in this article, which should be emphasized, demonstrated that the techniques mentioned above were suitable for the cited faults, whose comparison between the techniques showed the suitability of each one. en_US
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers, Inc. en_US
dc.relation.uri https://ieeexplore.ieee.org/xpl/conhome/7818135/proceeding en_US
dc.subject Induction motors en_US
dc.subject Predictive maintenance en_US
dc.subject MCSA en_US
dc.subject MSCSA en_US
dc.subject Fault diagnosis en_US
dc.title Comparison of techniques based on current signature analysis to fault detection and diagnosis in induction electrical motors en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty Engineering en_US
dc.identifier.department Department of Electrical Engineering en_US
dc.identifier.year 2016 en_US
dc.identifier.conference 1st International Conference on Electrical Engineering 2016 en_US
dc.identifier.place Colombo en_US
dc.identifier.pgnos pp. 74-79 en_US
dc.identifier.proceeding Proceedings of 1st International Conference on Electrical Engineering 2016 en_US
dc.identifier.email abrahaofontes@gmail.com en_US
dc.identifier.email carlosvcardoso@gmail.com en_US
dc.identifier.email lpedro@ufs.br en_US


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