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Study on fault diagnosis of broken rotor bars in squirrel cage induction motors: a multi‐agent system approach using intelligent classifiers.

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Title: Study on fault diagnosis of broken rotor bars in squirrel cage induction motors: a multi‐agent system approach using intelligent classifiers.
Authors: Drakaki, Maria1, Karnavas, Yannis L.2, karnavas@ee.duth.gr, Karlis, Athanasios D.2, Chasiotis, Ioannis D.2, Tzionas, Panagiotis1
Source: IET Electric Power Applications (Wiley-Blackwell); Feb2020, Vol. 14 Issue 2, p245-255, 11p
Database: Applied Science & Technology Source
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  Availability: 0
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DbLabel: Applied Science & Technology Source
An: 183841385
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PubType: Academic Journal
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  Data: <searchLink fieldCode="JN" term="%22IET+Electric+Power+Applications+%28Wiley-Blackwell%29%22">IET Electric Power Applications (Wiley-Blackwell)</searchLink>; Feb2020, Vol. 14 Issue 2, p245-255, 11p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=183841385
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      – Type: doi
        Value: 10.1049/iet-epa.2019.0619
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 245
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      – TitleFull: Study on fault diagnosis of broken rotor bars in squirrel cage induction motors: a multi‐agent system approach using intelligent classifiers.
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            NameFull: Drakaki, Maria
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            NameFull: Karnavas, Yannis L.
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            NameFull: Karlis, Athanasios D.
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            NameFull: Chasiotis, Ioannis D.
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              Text: Feb2020
              Type: published
              Y: 2020
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              Value: 14
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